├── README.md ├── algorithms.py ├── dataset ├── LCC │ ├── LCC_test.txt │ └── LCC_train.txt ├── LCC_EX │ ├── LCC_EX_test.txt │ └── LCC_EX_train.txt ├── MDS │ ├── MDS_test.txt │ └── MDS_train.txt ├── MDS_EX │ ├── MDS_EX_test.txt │ └── MDS_EX_train.txt ├── MUTAG │ ├── 10fold_idx │ │ ├── test_idx-1.txt │ │ ├── test_idx-10.txt │ │ ├── test_idx-2.txt │ │ ├── test_idx-3.txt │ │ ├── test_idx-4.txt │ │ ├── test_idx-5.txt │ │ ├── test_idx-6.txt │ │ ├── test_idx-7.txt │ │ ├── test_idx-8.txt │ │ ├── test_idx-9.txt │ │ ├── train_idx-1.txt │ │ ├── train_idx-10.txt │ │ ├── train_idx-2.txt │ │ ├── train_idx-3.txt │ │ ├── train_idx-4.txt │ │ ├── train_idx-5.txt │ │ ├── train_idx-6.txt │ │ ├── train_idx-7.txt │ │ ├── train_idx-8.txt │ │ └── train_idx-9.txt │ ├── MUTAG.mat │ └── MUTAG.txt ├── NCI1 │ ├── 10fold_idx │ │ ├── test_idx-1.txt │ │ ├── test_idx-10.txt │ │ ├── test_idx-2.txt │ │ ├── test_idx-3.txt │ │ ├── test_idx-4.txt │ │ ├── test_idx-5.txt │ │ ├── test_idx-6.txt │ │ ├── test_idx-7.txt │ │ ├── test_idx-8.txt │ │ ├── test_idx-9.txt │ │ ├── train_idx-1.txt │ │ ├── train_idx-10.txt │ │ ├── train_idx-2.txt │ │ ├── train_idx-3.txt │ │ ├── train_idx-4.txt │ │ ├── train_idx-5.txt │ │ ├── train_idx-6.txt │ │ ├── train_idx-7.txt │ │ ├── train_idx-8.txt │ │ └── train_idx-9.txt │ ├── NCI1.mat │ └── NCI1.txt ├── PROTEINS │ ├── 10fold_idx │ │ ├── test_idx-1.txt │ │ ├── test_idx-10.txt │ │ ├── test_idx-2.txt │ │ ├── test_idx-3.txt │ │ ├── test_idx-4.txt │ │ ├── test_idx-5.txt │ │ ├── test_idx-6.txt │ │ ├── test_idx-7.txt │ │ ├── test_idx-8.txt │ │ ├── test_idx-9.txt │ │ ├── train_idx-1.txt │ │ ├── train_idx-10.txt │ │ ├── train_idx-2.txt │ │ ├── train_idx-3.txt │ │ ├── train_idx-4.txt │ │ ├── train_idx-5.txt │ │ ├── train_idx-6.txt │ │ ├── train_idx-7.txt │ │ ├── train_idx-8.txt │ │ └── train_idx-9.txt │ ├── PROTEINS.mat │ └── PROTEINS.txt ├── TRIANGLE │ ├── TRIANGLE_test.txt │ └── TRIANGLE_train.txt └── TRIANGLE_EX │ ├── TRIANGLE_EX_test.txt │ └── TRIANGLE_EX_train.txt ├── dataset_gen.py ├── exec.sh ├── main.py ├── models ├── graphcnn.py └── mlp.py ├── requirements.txt └── util.py /README.md: -------------------------------------------------------------------------------- 1 | # Graph Neural Networks with Random Features (SDM 2021) 2 | 3 | We prove that adding random features to each node strengthens the expressive power of graph neural networks. The complete paper including appendices is available on [arXiv](https://arxiv.org/abs/2002.03155). 4 | 5 | This repository is forked from https://github.com/weihua916/powerful-gnns 6 | 7 | 8 | ## Installation 9 | 10 | Install PyTorch following the instuctions on the [official website] (https://pytorch.org/). The code has been tested over PyTorch 0.4.1 and 1.0.0 versions. 11 | 12 | Then install the other dependencies. 13 | ``` 14 | pip install -r requirements.txt 15 | ``` 16 | 17 | ## Test run 18 | 19 | ``` 20 | $ bash exec.sh 21 | ``` 22 | 23 | ## Datasets 24 | 25 | We release new synthetic benchmarks we used in the paper. All datasets are stored in the `dataset` directory. Each file describes a set of graphs with the following format: 26 | 27 | ``` 28 | s 0 29 | G_1 30 | G_2 31 | ... 32 | G_s 33 | ``` 34 | 35 | where `s` is the number of graphs, there is `0` for compatibility with graph classification datasets, `G_i` describes a graph with the following format: 36 | 37 | ``` 38 | n 39 | y_1 d_1 v_{1, 1} v_{1, 2} ... v_{1, d_1} 40 | y_2 d_2 v_{2, 1} v_{2, 2} ... v_{2, d_2} 41 | ... 42 | y_n d_n v_{n, 1} v_{n, 2} ... v_{n, d_n} 43 | ``` 44 | 45 | where `n` is the number of nodes, the `(i+2)`-th line describes node `i` (node ids are 0-indexed), `y_i` is the label of node `i`, `d_i` is the degree of node `i`, `v_{i, j}` is the `j`-th neighbor of node `i`. 46 | 47 | You can also generate (more) synthetic datasets by `dataset_gen.py`. 48 | 49 | The MDS datasets do not contain the label information (i.e., `y_i = 0` for all `i`). We generate labels dynamically using `algorithms.py` when we test models. 50 | 51 | ## Feedback and Contact 52 | 53 | Please feel free to contact me at r.sato AT ml.ist.i.kyoto-u.ac.jp, or to open issues. 54 | 55 | ## Citation 56 | 57 | ``` 58 | @inproceedings{sato2021random, 59 | author = {Ryoma Sato and Makoto Yamada and Hisashi Kashima}, 60 | title = {Random Features Strengthen Graph Neural Networks}, 61 | booktitle = {Proceedings of the 2021 {SIAM} International Conference on Data Mining, {SDM}}, 62 | year = {2021}, 63 | } 64 | ``` -------------------------------------------------------------------------------- /algorithms.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | def MDS_LOCAL(model, graphs): 4 | """ A greedy algorithm for the minimum dominating set problem. 5 | """ 6 | labels = [] 7 | cnt = 0 8 | for graph in graphs: 9 | n = len(graph.node_tags) 10 | l = [0 for i in range(n)] 11 | r = model.r.reshape(-1)[cnt:cnt+n] # use the same random features as the GNN model 12 | order = r.argsort().tolist() 13 | for p in [3, 2, 1]: 14 | # we assume the maximum degree is three 15 | for i in order: 16 | if l[i] == 1: 17 | continue 18 | q = 0 19 | for j in graph.neighbors[i]: 20 | if l[j] + sum([l[k] for k in graph.neighbors[j]]) == 0: 21 | q += 1 22 | if p == q: 23 | l[i] = 1 24 | labels += l 25 | cnt += n 26 | return labels 27 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-1.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-10.txt: -------------------------------------------------------------------------------- 1 | 81 2 | 55 3 | 59 4 | 149 5 | 24 6 | 157 7 | 29 8 | 3 9 | 78 10 | 16 11 | 86 12 | 35 13 | 136 14 | 166 15 | 6 16 | 4 17 | 39 18 | 96 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-2.txt: -------------------------------------------------------------------------------- 1 | 21 2 | 1 3 | 48 4 | 110 5 | 183 6 | 171 7 | 84 8 | 20 9 | 42 10 | 10 11 | 138 12 | 46 13 | 31 14 | 68 15 | 57 16 | 43 17 | 87 18 | 121 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-3.txt: -------------------------------------------------------------------------------- 1 | 104 2 | 60 3 | 65 4 | 120 5 | 61 6 | 167 7 | 17 8 | 179 9 | 127 10 | 18 11 | 101 12 | 8 13 | 14 14 | 33 15 | 53 16 | 181 17 | 41 18 | 185 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-4.txt: -------------------------------------------------------------------------------- 1 | 63 2 | 19 3 | 70 4 | 186 5 | 82 6 | 132 7 | 105 8 | 30 9 | 184 10 | 67 11 | 178 12 | 153 13 | 173 14 | 45 15 | 5 16 | 160 17 | 26 18 | 36 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-5.txt: -------------------------------------------------------------------------------- 1 | 75 2 | 25 3 | 134 4 | 66 5 | 12 6 | 150 7 | 108 8 | 175 9 | 79 10 | 47 11 | 11 12 | 76 13 | 116 14 | 124 15 | 162 16 | 22 17 | 148 18 | 93 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-6.txt: -------------------------------------------------------------------------------- 1 | 172 2 | 92 3 | 107 4 | 89 5 | 128 6 | 83 7 | 90 8 | 130 9 | 69 10 | 54 11 | 77 12 | 154 13 | 72 14 | 168 15 | 23 16 | 129 17 | 131 18 | 74 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-7.txt: -------------------------------------------------------------------------------- 1 | 146 2 | 177 3 | 112 4 | 137 5 | 94 6 | 64 7 | 176 8 | 71 9 | 143 10 | 158 11 | 15 12 | 80 13 | 145 14 | 85 15 | 103 16 | 32 17 | 62 18 | 88 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-8.txt: -------------------------------------------------------------------------------- 1 | 28 2 | 40 3 | 118 4 | 52 5 | 122 6 | 152 7 | 164 8 | 27 9 | 2 10 | 156 11 | 139 12 | 174 13 | 56 14 | 50 15 | 111 16 | 123 17 | 147 18 | 100 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/test_idx-9.txt: -------------------------------------------------------------------------------- 1 | 9 2 | 51 3 | 102 4 | 142 5 | 141 6 | 180 7 | 155 8 | 159 9 | 98 10 | 49 11 | 165 12 | 95 13 | 115 14 | 113 15 | 140 16 | 106 17 | 125 18 | 99 19 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-1.txt: -------------------------------------------------------------------------------- 1 | 21 2 | 1 3 | 48 4 | 110 5 | 183 6 | 171 7 | 84 8 | 20 9 | 42 10 | 10 11 | 138 12 | 46 13 | 31 14 | 68 15 | 57 16 | 43 17 | 87 18 | 121 19 | 104 20 | 60 21 | 65 22 | 120 23 | 61 24 | 167 25 | 17 26 | 179 27 | 127 28 | 18 29 | 101 30 | 8 31 | 14 32 | 33 33 | 53 34 | 181 35 | 41 36 | 185 37 | 63 38 | 19 39 | 70 40 | 186 41 | 82 42 | 132 43 | 105 44 | 30 45 | 184 46 | 67 47 | 178 48 | 153 49 | 173 50 | 45 51 | 5 52 | 160 53 | 26 54 | 36 55 | 75 56 | 25 57 | 134 58 | 66 59 | 12 60 | 150 61 | 108 62 | 175 63 | 79 64 | 47 65 | 11 66 | 76 67 | 116 68 | 124 69 | 162 70 | 22 71 | 148 72 | 93 73 | 172 74 | 92 75 | 107 76 | 89 77 | 128 78 | 83 79 | 90 80 | 130 81 | 69 82 | 54 83 | 77 84 | 154 85 | 72 86 | 168 87 | 23 88 | 129 89 | 131 90 | 74 91 | 146 92 | 177 93 | 112 94 | 137 95 | 94 96 | 64 97 | 176 98 | 71 99 | 143 100 | 158 101 | 15 102 | 80 103 | 145 104 | 85 105 | 103 106 | 32 107 | 62 108 | 88 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-10.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 63 56 | 19 57 | 70 58 | 186 59 | 82 60 | 132 61 | 105 62 | 30 63 | 184 64 | 67 65 | 178 66 | 153 67 | 173 68 | 45 69 | 5 70 | 160 71 | 26 72 | 36 73 | 75 74 | 25 75 | 134 76 | 66 77 | 12 78 | 150 79 | 108 80 | 175 81 | 79 82 | 47 83 | 11 84 | 76 85 | 116 86 | 124 87 | 162 88 | 22 89 | 148 90 | 93 91 | 172 92 | 92 93 | 107 94 | 89 95 | 128 96 | 83 97 | 90 98 | 130 99 | 69 100 | 54 101 | 77 102 | 154 103 | 72 104 | 168 105 | 23 106 | 129 107 | 131 108 | 74 109 | 146 110 | 177 111 | 112 112 | 137 113 | 94 114 | 64 115 | 176 116 | 71 117 | 143 118 | 158 119 | 15 120 | 80 121 | 145 122 | 85 123 | 103 124 | 32 125 | 62 126 | 88 127 | 28 128 | 40 129 | 118 130 | 52 131 | 122 132 | 152 133 | 164 134 | 27 135 | 2 136 | 156 137 | 139 138 | 174 139 | 56 140 | 50 141 | 111 142 | 123 143 | 147 144 | 100 145 | 9 146 | 51 147 | 102 148 | 142 149 | 141 150 | 180 151 | 155 152 | 159 153 | 98 154 | 49 155 | 165 156 | 95 157 | 115 158 | 113 159 | 140 160 | 106 161 | 125 162 | 99 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-2.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 104 20 | 60 21 | 65 22 | 120 23 | 61 24 | 167 25 | 17 26 | 179 27 | 127 28 | 18 29 | 101 30 | 8 31 | 14 32 | 33 33 | 53 34 | 181 35 | 41 36 | 185 37 | 63 38 | 19 39 | 70 40 | 186 41 | 82 42 | 132 43 | 105 44 | 30 45 | 184 46 | 67 47 | 178 48 | 153 49 | 173 50 | 45 51 | 5 52 | 160 53 | 26 54 | 36 55 | 75 56 | 25 57 | 134 58 | 66 59 | 12 60 | 150 61 | 108 62 | 175 63 | 79 64 | 47 65 | 11 66 | 76 67 | 116 68 | 124 69 | 162 70 | 22 71 | 148 72 | 93 73 | 172 74 | 92 75 | 107 76 | 89 77 | 128 78 | 83 79 | 90 80 | 130 81 | 69 82 | 54 83 | 77 84 | 154 85 | 72 86 | 168 87 | 23 88 | 129 89 | 131 90 | 74 91 | 146 92 | 177 93 | 112 94 | 137 95 | 94 96 | 64 97 | 176 98 | 71 99 | 143 100 | 158 101 | 15 102 | 80 103 | 145 104 | 85 105 | 103 106 | 32 107 | 62 108 | 88 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-3.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 63 38 | 19 39 | 70 40 | 186 41 | 82 42 | 132 43 | 105 44 | 30 45 | 184 46 | 67 47 | 178 48 | 153 49 | 173 50 | 45 51 | 5 52 | 160 53 | 26 54 | 36 55 | 75 56 | 25 57 | 134 58 | 66 59 | 12 60 | 150 61 | 108 62 | 175 63 | 79 64 | 47 65 | 11 66 | 76 67 | 116 68 | 124 69 | 162 70 | 22 71 | 148 72 | 93 73 | 172 74 | 92 75 | 107 76 | 89 77 | 128 78 | 83 79 | 90 80 | 130 81 | 69 82 | 54 83 | 77 84 | 154 85 | 72 86 | 168 87 | 23 88 | 129 89 | 131 90 | 74 91 | 146 92 | 177 93 | 112 94 | 137 95 | 94 96 | 64 97 | 176 98 | 71 99 | 143 100 | 158 101 | 15 102 | 80 103 | 145 104 | 85 105 | 103 106 | 32 107 | 62 108 | 88 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-4.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 75 56 | 25 57 | 134 58 | 66 59 | 12 60 | 150 61 | 108 62 | 175 63 | 79 64 | 47 65 | 11 66 | 76 67 | 116 68 | 124 69 | 162 70 | 22 71 | 148 72 | 93 73 | 172 74 | 92 75 | 107 76 | 89 77 | 128 78 | 83 79 | 90 80 | 130 81 | 69 82 | 54 83 | 77 84 | 154 85 | 72 86 | 168 87 | 23 88 | 129 89 | 131 90 | 74 91 | 146 92 | 177 93 | 112 94 | 137 95 | 94 96 | 64 97 | 176 98 | 71 99 | 143 100 | 158 101 | 15 102 | 80 103 | 145 104 | 85 105 | 103 106 | 32 107 | 62 108 | 88 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-5.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 63 56 | 19 57 | 70 58 | 186 59 | 82 60 | 132 61 | 105 62 | 30 63 | 184 64 | 67 65 | 178 66 | 153 67 | 173 68 | 45 69 | 5 70 | 160 71 | 26 72 | 36 73 | 172 74 | 92 75 | 107 76 | 89 77 | 128 78 | 83 79 | 90 80 | 130 81 | 69 82 | 54 83 | 77 84 | 154 85 | 72 86 | 168 87 | 23 88 | 129 89 | 131 90 | 74 91 | 146 92 | 177 93 | 112 94 | 137 95 | 94 96 | 64 97 | 176 98 | 71 99 | 143 100 | 158 101 | 15 102 | 80 103 | 145 104 | 85 105 | 103 106 | 32 107 | 62 108 | 88 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-6.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 63 56 | 19 57 | 70 58 | 186 59 | 82 60 | 132 61 | 105 62 | 30 63 | 184 64 | 67 65 | 178 66 | 153 67 | 173 68 | 45 69 | 5 70 | 160 71 | 26 72 | 36 73 | 75 74 | 25 75 | 134 76 | 66 77 | 12 78 | 150 79 | 108 80 | 175 81 | 79 82 | 47 83 | 11 84 | 76 85 | 116 86 | 124 87 | 162 88 | 22 89 | 148 90 | 93 91 | 146 92 | 177 93 | 112 94 | 137 95 | 94 96 | 64 97 | 176 98 | 71 99 | 143 100 | 158 101 | 15 102 | 80 103 | 145 104 | 85 105 | 103 106 | 32 107 | 62 108 | 88 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-7.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 63 56 | 19 57 | 70 58 | 186 59 | 82 60 | 132 61 | 105 62 | 30 63 | 184 64 | 67 65 | 178 66 | 153 67 | 173 68 | 45 69 | 5 70 | 160 71 | 26 72 | 36 73 | 75 74 | 25 75 | 134 76 | 66 77 | 12 78 | 150 79 | 108 80 | 175 81 | 79 82 | 47 83 | 11 84 | 76 85 | 116 86 | 124 87 | 162 88 | 22 89 | 148 90 | 93 91 | 172 92 | 92 93 | 107 94 | 89 95 | 128 96 | 83 97 | 90 98 | 130 99 | 69 100 | 54 101 | 77 102 | 154 103 | 72 104 | 168 105 | 23 106 | 129 107 | 131 108 | 74 109 | 28 110 | 40 111 | 118 112 | 52 113 | 122 114 | 152 115 | 164 116 | 27 117 | 2 118 | 156 119 | 139 120 | 174 121 | 56 122 | 50 123 | 111 124 | 123 125 | 147 126 | 100 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-8.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 63 56 | 19 57 | 70 58 | 186 59 | 82 60 | 132 61 | 105 62 | 30 63 | 184 64 | 67 65 | 178 66 | 153 67 | 173 68 | 45 69 | 5 70 | 160 71 | 26 72 | 36 73 | 75 74 | 25 75 | 134 76 | 66 77 | 12 78 | 150 79 | 108 80 | 175 81 | 79 82 | 47 83 | 11 84 | 76 85 | 116 86 | 124 87 | 162 88 | 22 89 | 148 90 | 93 91 | 172 92 | 92 93 | 107 94 | 89 95 | 128 96 | 83 97 | 90 98 | 130 99 | 69 100 | 54 101 | 77 102 | 154 103 | 72 104 | 168 105 | 23 106 | 129 107 | 131 108 | 74 109 | 146 110 | 177 111 | 112 112 | 137 113 | 94 114 | 64 115 | 176 116 | 71 117 | 143 118 | 158 119 | 15 120 | 80 121 | 145 122 | 85 123 | 103 124 | 32 125 | 62 126 | 88 127 | 9 128 | 51 129 | 102 130 | 142 131 | 141 132 | 180 133 | 155 134 | 159 135 | 98 136 | 49 137 | 165 138 | 95 139 | 115 140 | 113 141 | 140 142 | 106 143 | 125 144 | 99 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/10fold_idx/train_idx-9.txt: -------------------------------------------------------------------------------- 1 | 109 2 | 126 3 | 182 4 | 38 5 | 170 6 | 91 7 | 135 8 | 119 9 | 58 10 | 44 11 | 7 12 | 144 13 | 133 14 | 117 15 | 151 16 | 34 17 | 97 18 | 73 19 | 21 20 | 1 21 | 48 22 | 110 23 | 183 24 | 171 25 | 84 26 | 20 27 | 42 28 | 10 29 | 138 30 | 46 31 | 31 32 | 68 33 | 57 34 | 43 35 | 87 36 | 121 37 | 104 38 | 60 39 | 65 40 | 120 41 | 61 42 | 167 43 | 17 44 | 179 45 | 127 46 | 18 47 | 101 48 | 8 49 | 14 50 | 33 51 | 53 52 | 181 53 | 41 54 | 185 55 | 63 56 | 19 57 | 70 58 | 186 59 | 82 60 | 132 61 | 105 62 | 30 63 | 184 64 | 67 65 | 178 66 | 153 67 | 173 68 | 45 69 | 5 70 | 160 71 | 26 72 | 36 73 | 75 74 | 25 75 | 134 76 | 66 77 | 12 78 | 150 79 | 108 80 | 175 81 | 79 82 | 47 83 | 11 84 | 76 85 | 116 86 | 124 87 | 162 88 | 22 89 | 148 90 | 93 91 | 172 92 | 92 93 | 107 94 | 89 95 | 128 96 | 83 97 | 90 98 | 130 99 | 69 100 | 54 101 | 77 102 | 154 103 | 72 104 | 168 105 | 23 106 | 129 107 | 131 108 | 74 109 | 146 110 | 177 111 | 112 112 | 137 113 | 94 114 | 64 115 | 176 116 | 71 117 | 143 118 | 158 119 | 15 120 | 80 121 | 145 122 | 85 123 | 103 124 | 32 125 | 62 126 | 88 127 | 28 128 | 40 129 | 118 130 | 52 131 | 122 132 | 152 133 | 164 134 | 27 135 | 2 136 | 156 137 | 139 138 | 174 139 | 56 140 | 50 141 | 111 142 | 123 143 | 147 144 | 100 145 | 81 146 | 55 147 | 59 148 | 149 149 | 24 150 | 157 151 | 29 152 | 3 153 | 78 154 | 16 155 | 86 156 | 35 157 | 136 158 | 166 159 | 6 160 | 4 161 | 39 162 | 96 163 | 163 164 | 37 165 | 13 166 | 169 167 | 187 168 | 114 169 | 161 170 | 0 171 | -------------------------------------------------------------------------------- /dataset/MUTAG/MUTAG.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joisino/random-features/180e926edd601e10d4b115a0a1722fa7183f7f13/dataset/MUTAG/MUTAG.mat -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-1.txt: -------------------------------------------------------------------------------- 1 | 2585 2 | 3444 3 | 1247 4 | 3872 5 | 63 6 | 155 7 | 3770 8 | 257 9 | 1439 10 | 247 11 | 3716 12 | 1103 13 | 2082 14 | 615 15 | 1970 16 | 2246 17 | 2231 18 | 607 19 | 453 20 | 1051 21 | 1386 22 | 1582 23 | 3986 24 | 3774 25 | 1801 26 | 3829 27 | 1710 28 | 1930 29 | 1436 30 | 1596 31 | 3723 32 | 3979 33 | 1058 34 | 633 35 | 96 36 | 1685 37 | 772 38 | 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-------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-10.txt: -------------------------------------------------------------------------------- 1 | 26 2 | 688 3 | 3651 4 | 561 5 | 2174 6 | 479 7 | 541 8 | 991 9 | 2375 10 | 1315 11 | 3769 12 | 2304 13 | 1175 14 | 532 15 | 3110 16 | 2535 17 | 2511 18 | 1448 19 | 2830 20 | 3269 21 | 222 22 | 3740 23 | 1379 24 | 1369 25 | 1190 26 | 3263 27 | 1411 28 | 3830 29 | 1534 30 | 3093 31 | 2350 32 | 513 33 | 2444 34 | 1064 35 | 1750 36 | 1884 37 | 2990 38 | 2153 39 | 967 40 | 1610 41 | 113 42 | 3331 43 | 870 44 | 3106 45 | 3017 46 | 3440 47 | 2831 48 | 2288 49 | 2342 50 | 89 51 | 2387 52 | 3036 53 | 2027 54 | 279 55 | 1238 56 | 2023 57 | 2848 58 | 2946 59 | 1746 60 | 1009 61 | 3863 62 | 2906 63 | 2201 64 | 2245 65 | 840 66 | 170 67 | 1095 68 | 2382 69 | 2560 70 | 2011 71 | 1838 72 | 3734 73 | 624 74 | 1854 75 | 3766 76 | 3551 77 | 917 78 | 4100 79 | 3783 80 | 2936 81 | 979 82 | 1779 83 | 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272 | 848 273 | 3670 274 | 1802 275 | 1144 276 | 2792 277 | 2816 278 | 2900 279 | 952 280 | 2874 281 | 3326 282 | 1825 283 | 746 284 | 1631 285 | 1042 286 | 2125 287 | 2085 288 | 3570 289 | 2249 290 | 3895 291 | 446 292 | 3321 293 | 726 294 | 1044 295 | 506 296 | 1946 297 | 3600 298 | 2860 299 | 2836 300 | 3553 301 | 2904 302 | 3729 303 | 2319 304 | 4024 305 | 3568 306 | 3328 307 | 2362 308 | 2267 309 | 672 310 | 371 311 | 1856 312 | 2469 313 | 3525 314 | 701 315 | 2064 316 | 679 317 | 121 318 | 1498 319 | 2491 320 | 3536 321 | 283 322 | 1722 323 | 3336 324 | 180 325 | 3707 326 | 1256 327 | 730 328 | 2013 329 | 1857 330 | 928 331 | 2488 332 | 2240 333 | 1783 334 | 1198 335 | 1433 336 | 1407 337 | 1492 338 | 916 339 | 4067 340 | 1055 341 | 3026 342 | 3562 343 | 3052 344 | 2485 345 | 3982 346 | 693 347 | 3866 348 | 977 349 | 3361 350 | 3030 351 | 1812 352 | 430 353 | 2774 354 | 2377 355 | 1634 356 | 1676 357 | 2945 358 | 2501 359 | 2092 360 | 2718 361 | 1179 362 | 728 363 | 3745 364 | 2864 365 | 157 366 | 3368 367 | 214 368 | 2339 369 | 152 370 | 635 371 | 3455 372 | 727 373 | 2381 374 | 920 375 | 3063 376 | 300 377 | 3148 378 | 2097 379 | 2394 380 | 2315 381 | 2205 382 | 3427 383 | 2584 384 | 1910 385 | 3307 386 | 3287 387 | 858 388 | 1617 389 | 2262 390 | 1593 391 | 30 392 | 1230 393 | 1696 394 | 2504 395 | 2789 396 | 8 397 | 2070 398 | 2500 399 | 3937 400 | 2129 401 | 2351 402 | 345 403 | 1403 404 | 522 405 | 1728 406 | 644 407 | 3304 408 | 3800 409 | 199 410 | 1808 411 | 2832 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-2.txt: -------------------------------------------------------------------------------- 1 | 3426 2 | 2493 3 | 2943 4 | 1788 5 | 3238 6 | 1461 7 | 1120 8 | 1070 9 | 3897 10 | 3861 11 | 2758 12 | 1649 13 | 2405 14 | 1579 15 | 221 16 | 1763 17 | 647 18 | 2037 19 | 1036 20 | 1422 21 | 179 22 | 970 23 | 3856 24 | 2871 25 | 1901 26 | 2138 27 | 3753 28 | 2455 29 | 3352 30 | 3096 31 | 3771 32 | 32 33 | 2162 34 | 1087 35 | 1344 36 | 427 37 | 755 38 | 3523 39 | 43 40 | 1832 41 | 2152 42 | 1950 43 | 1687 44 | 692 45 | 1431 46 | 1136 47 | 557 48 | 458 49 | 1817 50 | 4076 51 | 48 52 | 110 53 | 1348 54 | 3736 55 | 2524 56 | 3277 57 | 3188 58 | 1068 59 | 3954 60 | 570 61 | 1430 62 | 1322 63 | 1391 64 | 845 65 | 957 66 | 369 67 | 2284 68 | 3809 69 | 1360 70 | 3545 71 | 3443 72 | 284 73 | 1302 74 | 411 75 | 490 76 | 1516 77 | 2034 78 | 857 79 | 3095 80 | 1111 81 | 653 82 | 1900 83 | 1148 84 | 3888 85 | 1712 86 | 1495 87 | 3907 88 | 2463 89 | 1942 90 | 1410 91 | 1274 92 | 2667 93 | 1408 94 | 1928 95 | 1224 96 | 1019 97 | 980 98 | 3372 99 | 1914 100 | 421 101 | 62 102 | 1155 103 | 1886 104 | 1721 105 | 2713 106 | 1994 107 | 1171 108 | 3779 109 | 2733 110 | 2363 111 | 3806 112 | 6 113 | 3422 114 | 3934 115 | 2818 116 | 1656 117 | 3676 118 | 3631 119 | 1978 120 | 582 121 | 3744 122 | 625 123 | 226 124 | 3381 125 | 3365 126 | 2598 127 | 4018 128 | 1288 129 | 2177 130 | 3663 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319 | 3978 320 | 1426 321 | 3792 322 | 3403 323 | 3928 324 | 2310 325 | 1139 326 | 879 327 | 3201 328 | 2433 329 | 3197 330 | 3339 331 | 2271 332 | 2617 333 | 1425 334 | 3335 335 | 3409 336 | 1075 337 | 2877 338 | 241 339 | 3131 340 | 1830 341 | 3778 342 | 3222 343 | 414 344 | 1409 345 | 2048 346 | 2142 347 | 329 348 | 2274 349 | 108 350 | 3129 351 | 3038 352 | 544 353 | 833 354 | 1955 355 | 2018 356 | 3108 357 | 1909 358 | 4035 359 | 540 360 | 3190 361 | 656 362 | 3456 363 | 654 364 | 3362 365 | 1515 366 | 894 367 | 429 368 | 296 369 | 3933 370 | 1211 371 | 1834 372 | 1012 373 | 2282 374 | 884 375 | 3916 376 | 1280 377 | 2582 378 | 3845 379 | 4097 380 | 3885 381 | 850 382 | 1414 383 | 3660 384 | 2739 385 | 315 386 | 70 387 | 3391 388 | 3606 389 | 3104 390 | 2494 391 | 1688 392 | 956 393 | 3454 394 | 2692 395 | 1740 396 | 2422 397 | 3250 398 | 2525 399 | 610 400 | 1166 401 | 1611 402 | 543 403 | 3602 404 | 1586 405 | 1606 406 | 465 407 | 972 408 | 1340 409 | 830 410 | 3948 411 | 1077 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-3.txt: -------------------------------------------------------------------------------- 1 | 3414 2 | 3180 3 | 1505 4 | 1669 5 | 1151 6 | 902 7 | 3167 8 | 2550 9 | 3118 10 | 1533 11 | 1996 12 | 2102 13 | 3284 14 | 823 15 | 3164 16 | 3166 17 | 581 18 | 1590 19 | 198 20 | 2709 21 | 501 22 | 2878 23 | 2089 24 | 2210 25 | 409 26 | 3881 27 | 1402 28 | 1118 29 | 1522 30 | 2515 31 | 1327 32 | 3196 33 | 1048 34 | 1551 35 | 2609 36 | 652 37 | 1104 38 | 3946 39 | 2891 40 | 1065 41 | 2265 42 | 2131 43 | 2414 44 | 3494 45 | 518 46 | 1291 47 | 898 48 | 3099 49 | 3522 50 | 323 51 | 1699 52 | 1709 53 | 1484 54 | 351 55 | 1530 56 | 2434 57 | 436 58 | 1787 59 | 1045 60 | 2442 61 | 2998 62 | 3735 63 | 2117 64 | 415 65 | 142 66 | 3208 67 | 2215 68 | 1664 69 | 2522 70 | 60 71 | 2337 72 | 1512 73 | 1868 74 | 1377 75 | 1254 76 | 2627 77 | 196 78 | 2252 79 | 3045 80 | 2813 81 | 2548 82 | 616 83 | 3788 84 | 1231 85 | 1997 86 | 1060 87 | 1558 88 | 2688 89 | 2841 90 | 2508 91 | 819 92 | 3249 93 | 2218 94 | 3114 95 | 4062 96 | 472 97 | 3087 98 | 2194 99 | 78 100 | 1553 101 | 2312 102 | 987 103 | 2470 104 | 2559 105 | 1381 106 | 1011 107 | 3818 108 | 3351 109 | 3847 110 | 2331 111 | 1341 112 | 2926 113 | 1913 114 | 2769 115 | 2984 116 | 3798 117 | 1268 118 | 921 119 | 1203 120 | 3048 121 | 209 122 | 1882 123 | 2852 124 | 3537 125 | 1792 126 | 1494 127 | 1971 128 | 2199 129 | 2148 130 | 614 131 | 468 132 | 3267 133 | 2429 134 | 2935 135 | 385 136 | 3279 137 | 3105 138 | 290 139 | 2400 140 | 3484 141 | 705 142 | 2570 143 | 1396 144 | 275 145 | 908 146 | 1093 147 | 1489 148 | 2778 149 | 2313 150 | 2590 151 | 3313 152 | 3296 153 | 1940 154 | 3672 155 | 3101 156 | 2894 157 | 1142 158 | 1527 159 | 2302 160 | 1777 161 | 3311 162 | 686 163 | 138 164 | 2624 165 | 3977 166 | 2897 167 | 3852 168 | 1577 169 | 1388 170 | 1221 171 | 587 172 | 1899 173 | 1877 174 | 1222 175 | 216 176 | 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271 | 536 272 | 91 273 | 3975 274 | 1639 275 | 3804 276 | 3319 277 | 3382 278 | 3594 279 | 2318 280 | 2195 281 | 1138 282 | 2538 283 | 1744 284 | 3459 285 | 3904 286 | 3271 287 | 3714 288 | 1791 289 | 3931 290 | 3310 291 | 3241 292 | 3544 293 | 3575 294 | 3260 295 | 2835 296 | 311 297 | 2594 298 | 1496 299 | 2708 300 | 3390 301 | 2035 302 | 704 303 | 3659 304 | 4082 305 | 186 306 | 3125 307 | 3452 308 | 2388 309 | 1948 310 | 797 311 | 1983 312 | 2226 313 | 3342 314 | 41 315 | 1069 316 | 2687 317 | 2259 318 | 265 319 | 1258 320 | 1170 321 | 2651 322 | 3765 323 | 3153 324 | 1575 325 | 669 326 | 2741 327 | 1039 328 | 3450 329 | 2232 330 | 2150 331 | 1297 332 | 3035 333 | 3822 334 | 2542 335 | 2947 336 | 2067 337 | 2101 338 | 3154 339 | 2908 340 | 2440 341 | 1301 342 | 3220 343 | 401 344 | 372 345 | 3610 346 | 3286 347 | 417 348 | 926 349 | 664 350 | 1354 351 | 678 352 | 500 353 | 3945 354 | 1355 355 | 3212 356 | 2443 357 | 416 358 | 3919 359 | 4041 360 | 1061 361 | 1545 362 | 1267 363 | 260 364 | 2921 365 | 2674 366 | 799 367 | 1261 368 | 1624 369 | 645 370 | 3566 371 | 1062 372 | 1215 373 | 2639 374 | 3270 375 | 3980 376 | 2678 377 | 3929 378 | 3685 379 | 476 380 | 3324 381 | 3373 382 | 2402 383 | 1957 384 | 604 385 | 2305 386 | 72 387 | 609 388 | 1306 389 | 525 390 | 2918 391 | 480 392 | 4021 393 | 1707 394 | 2022 395 | 720 396 | 3370 397 | 340 398 | 3145 399 | 1980 400 | 3278 401 | 3184 402 | 603 403 | 2784 404 | 212 405 | 4054 406 | 969 407 | 905 408 | 2892 409 | 2717 410 | 2551 411 | 94 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-4.txt: -------------------------------------------------------------------------------- 1 | 584 2 | 3018 3 | 2595 4 | 2738 5 | 619 6 | 1239 7 | 1083 8 | 2454 9 | 1518 10 | 3917 11 | 166 12 | 1487 13 | 1621 14 | 1745 15 | 1636 16 | 3113 17 | 2206 18 | 1591 19 | 1729 20 | 3843 21 | 1147 22 | 2975 23 | 4098 24 | 1427 25 | 1697 26 | 2056 27 | 3750 28 | 3499 29 | 307 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223 | 459 224 | 3875 225 | 3449 226 | 1476 227 | 2509 228 | 2631 229 | 2973 230 | 2446 231 | 527 232 | 643 233 | 779 234 | 1663 235 | 3433 236 | 740 237 | 3690 238 | 2184 239 | 3984 240 | 441 241 | 1474 242 | 3943 243 | 105 244 | 2372 245 | 1855 246 | 2086 247 | 3944 248 | 1626 249 | 1672 250 | 2955 251 | 3213 252 | 743 253 | 1800 254 | 7 255 | 462 256 | 787 257 | 3295 258 | 1206 259 | 696 260 | 1867 261 | 2972 262 | 3942 263 | 534 264 | 1346 265 | 1052 266 | 2009 267 | 2452 268 | 2229 269 | 1816 270 | 721 271 | 1690 272 | 1469 273 | 2850 274 | 2170 275 | 3363 276 | 1157 277 | 2567 278 | 2838 279 | 1178 280 | 1961 281 | 76 282 | 1001 283 | 994 284 | 2572 285 | 3956 286 | 3327 287 | 3224 288 | 1885 289 | 802 290 | 3189 291 | 1758 292 | 2235 293 | 3865 294 | 228 295 | 3989 296 | 3496 297 | 2991 298 | 1513 299 | 1483 300 | 2425 301 | 3012 302 | 1964 303 | 1005 304 | 156 305 | 3329 306 | 3898 307 | 2604 308 | 4039 309 | 4052 310 | 1038 311 | 1066 312 | 1890 313 | 1137 314 | 1293 315 | 568 316 | 251 317 | 305 318 | 493 319 | 2577 320 | 193 321 | 2812 322 | 3517 323 | 795 324 | 3020 325 | 1115 326 | 2765 327 | 2072 328 | 657 329 | 2862 330 | 1580 331 | 2685 332 | 14 333 | 3057 334 | 1864 335 | 2730 336 | 3453 337 | 0 338 | 955 339 | 2694 340 | 2578 341 | 2449 342 | 1986 343 | 927 344 | 1693 345 | 3884 346 | 947 347 | 939 348 | 754 349 | 149 350 | 733 351 | 2207 352 | 3508 353 | 337 354 | 3178 355 | 2796 356 | 831 357 | 3140 358 | 3185 359 | 1393 360 | 3246 361 | 2821 362 | 3460 363 | 2787 364 | 3396 365 | 1428 366 | 3752 367 | 3060 368 | 3859 369 | 1299 370 | 3407 371 | 1998 372 | 1189 373 | 1646 374 | 81 375 | 1149 376 | 1129 377 | 1517 378 | 1197 379 | 709 380 | 1237 381 | 3667 382 | 1071 383 | 312 384 | 1454 385 | 171 386 | 535 387 | 2809 388 | 2782 389 | 1156 390 | 3346 391 | 3834 392 | 2428 393 | 2997 394 | 357 395 | 2300 396 | 2296 397 | 864 398 | 998 399 | 1874 400 | 3591 401 | 1902 402 | 3583 403 | 3120 404 | 2966 405 | 3951 406 | 3947 407 | 2907 408 | 3209 409 | 2044 410 | 1642 411 | 2958 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-5.txt: -------------------------------------------------------------------------------- 1 | 739 2 | 548 3 | 3720 4 | 2707 5 | 2137 6 | 2068 7 | 1383 8 | 514 9 | 3763 10 | 2564 11 | 2790 12 | 403 13 | 1126 14 | 348 15 | 1278 16 | 2187 17 | 3656 18 | 1839 19 | 909 20 | 3715 21 | 229 22 | 612 23 | 1134 24 | 1965 25 | 2546 26 | 1780 27 | 277 28 | 517 29 | 3958 30 | 3633 31 | 3712 32 | 565 33 | 2959 34 | 3521 35 | 3578 36 | 1705 37 | 2456 38 | 148 39 | 406 40 | 1418 41 | 3812 42 | 594 43 | 3679 44 | 2964 45 | 925 46 | 1557 47 | 2266 48 | 237 49 | 2132 50 | 1018 51 | 3061 52 | 736 53 | 3886 54 | 1399 55 | 402 56 | 3706 57 | 3971 58 | 558 59 | 993 60 | 3650 61 | 3316 62 | 488 63 | 1922 64 | 4077 65 | 3344 66 | 1843 67 | 1815 68 | 2317 69 | 3094 70 | 2202 71 | 1501 72 | 1645 73 | 1915 74 | 1017 75 | 3285 76 | 2233 77 | 3844 78 | 3738 79 | 3276 80 | 172 81 | 960 82 | 790 83 | 3144 84 | 3698 85 | 2867 86 | 1463 87 | 3577 88 | 3405 89 | 280 90 | 974 91 | 2297 92 | 3541 93 | 3033 94 | 2094 95 | 1116 96 | 813 97 | 1085 98 | 1380 99 | 2681 100 | 379 101 | 2842 102 | 3554 103 | 3492 104 | 2571 105 | 3705 106 | 3622 107 | 2756 108 | 2979 109 | 3882 110 | 375 111 | 489 112 | 2915 113 | 486 114 | 2495 115 | 3161 116 | 1990 117 | 684 118 | 464 119 | 1796 120 | 556 121 | 289 122 | 2208 123 | 4107 124 | 1795 125 | 712 126 | 2650 127 | 2712 128 | 3567 129 | 3001 130 | 523 131 | 383 132 | 1233 133 | 913 134 | 3921 135 | 1703 136 | 197 137 | 3889 138 | 2556 139 | 2378 140 | 316 141 | 16 142 | 1918 143 | 804 144 | 1789 145 | 80 146 | 2285 147 | 3111 148 | 4101 149 | 258 150 | 3617 151 | 3687 152 | 3349 153 | 1786 154 | 771 155 | 3437 156 | 1880 157 | 2409 158 | 3226 159 | 1613 160 | 1086 161 | 3625 162 | 1165 163 | 986 164 | 2136 165 | 2033 166 | 1836 167 | 3596 168 | 3681 169 | 2014 170 | 4026 171 | 1100 172 | 1195 173 | 286 174 | 2697 175 | 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1304 269 | 2937 270 | 2799 271 | 1574 272 | 4071 273 | 3532 274 | 984 275 | 2283 276 | 3837 277 | 2192 278 | 2155 279 | 1755 280 | 3923 281 | 877 282 | 1597 283 | 666 284 | 2970 285 | 2839 286 | 1572 287 | 3630 288 | 1528 289 | 3894 290 | 2066 291 | 1529 292 | 4027 293 | 1923 294 | 3375 295 | 2340 296 | 2804 297 | 1798 298 | 1004 299 | 780 300 | 520 301 | 788 302 | 3791 303 | 1450 304 | 457 305 | 1756 306 | 1605 307 | 1726 308 | 3369 309 | 2675 310 | 2075 311 | 3421 312 | 3535 313 | 1466 314 | 3582 315 | 2576 316 | 3573 317 | 3879 318 | 873 319 | 2146 320 | 1601 321 | 1678 322 | 1584 323 | 67 324 | 4025 325 | 1619 326 | 2078 327 | 1662 328 | 1488 329 | 2098 330 | 2278 331 | 302 332 | 1863 333 | 1318 334 | 2096 335 | 1002 336 | 1370 337 | 623 338 | 184 339 | 689 340 | 3794 341 | 1213 342 | 1842 343 | 3158 344 | 1878 345 | 2541 346 | 2303 347 | 4091 348 | 3819 349 | 3356 350 | 1342 351 | 224 352 | 2847 353 | 1535 354 | 22 355 | 3938 356 | 3662 357 | 3673 358 | 3049 359 | 147 360 | 2468 361 | 1660 362 | 2128 363 | 2100 364 | 1698 365 | 1641 366 | 2962 367 | 711 368 | 2968 369 | 3533 370 | 3985 371 | 2079 372 | 933 373 | 3780 374 | 1218 375 | 3139 376 | 658 377 | 391 378 | 364 379 | 1644 380 | 2981 381 | 3476 382 | 2728 383 | 3266 384 | 2800 385 | 2996 386 | 3790 387 | 1263 388 | 2189 389 | 2971 390 | 1655 391 | 1161 392 | 230 393 | 1932 394 | 3332 395 | 373 396 | 4089 397 | 1848 398 | 456 399 | 1905 400 | 189 401 | 3341 402 | 1620 403 | 3152 404 | 608 405 | 2083 406 | 3439 407 | 3378 408 | 834 409 | 2903 410 | 3655 411 | 3264 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-6.txt: -------------------------------------------------------------------------------- 1 | 2815 2 | 2942 3 | 943 4 | 2071 5 | 2106 6 | 335 7 | 2823 8 | 1465 9 | 3704 10 | 2953 11 | 3635 12 | 2182 13 | 1347 14 | 732 15 | 3487 16 | 116 17 | 3435 18 | 3475 19 | 1207 20 | 297 21 | 3309 22 | 445 23 | 1714 24 | 2706 25 | 1860 26 | 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219 | 3639 220 | 866 221 | 575 222 | 2566 223 | 232 224 | 2779 225 | 2672 226 | 2415 227 | 1921 228 | 213 229 | 215 230 | 15 231 | 1248 232 | 3590 233 | 904 234 | 2393 235 | 2227 236 | 3557 237 | 3446 238 | 4016 239 | 2683 240 | 236 241 | 3870 242 | 1319 243 | 1651 244 | 1387 245 | 309 246 | 2481 247 | 1674 248 | 2502 249 | 282 250 | 1067 251 | 852 252 | 2684 253 | 2801 254 | 162 255 | 1279 256 | 2467 257 | 930 258 | 591 259 | 3318 260 | 3654 261 | 2298 262 | 2243 263 | 3312 264 | 3644 265 | 2521 266 | 1295 267 | 2857 268 | 2191 269 | 1162 270 | 2608 271 | 1748 272 | 56 273 | 3471 274 | 1713 275 | 1050 276 | 93 277 | 1375 278 | 808 279 | 680 280 | 1608 281 | 1241 282 | 1622 283 | 542 284 | 2251 285 | 1031 286 | 1604 287 | 3940 288 | 1828 289 | 2327 290 | 1003 291 | 2228 292 | 3217 293 | 807 294 | 2038 295 | 1 296 | 3377 297 | 4050 298 | 2833 299 | 3965 300 | 2144 301 | 1733 302 | 818 303 | 3064 304 | 2190 305 | 3429 306 | 3005 307 | 2806 308 | 2746 309 | 3461 310 | 1245 311 | 3612 312 | 3530 313 | 321 314 | 2866 315 | 3091 316 | 2476 317 | 1350 318 | 1627 319 | 1502 320 | 2260 321 | 3000 322 | 1735 323 | 3787 324 | 586 325 | 2472 326 | 3348 327 | 399 328 | 1560 329 | 106 330 | 431 331 | 1898 332 | 3516 333 | 2166 334 | 1307 335 | 1897 336 | 3359 337 | 3425 338 | 805 339 | 3642 340 | 3354 341 | 207 342 | 1338 343 | 1794 344 | 3628 345 | 1101 346 | 3173 347 | 3840 348 | 1394 349 | 2794 350 | 3216 351 | 659 352 | 234 353 | 1967 354 | 3177 355 | 3228 356 | 3168 357 | 3683 358 | 3932 359 | 3803 360 | 3501 361 | 2490 362 | 2989 363 | 3481 364 | 3584 365 | 3031 366 | 3128 367 | 2344 368 | 3088 369 | 2558 370 | 2496 371 | 259 372 | 2669 373 | 1437 374 | 339 375 | 800 376 | 2807 377 | 3240 378 | 707 379 | 4003 380 | 1367 381 | 620 382 | 122 383 | 423 384 | 132 385 | 1947 386 | 2292 387 | 21 388 | 816 389 | 2910 390 | 2544 391 | 1332 392 | 1822 393 | 2289 394 | 3151 395 | 3430 396 | 3032 397 | 1470 398 | 3069 399 | 3853 400 | 1632 401 | 1753 402 | 206 403 | 673 404 | 2868 405 | 3624 406 | 2696 407 | 2055 408 | 1209 409 | 862 410 | 3016 411 | 1021 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-7.txt: -------------------------------------------------------------------------------- 1 | 1316 2 | 860 3 | 3550 4 | 3997 5 | 1937 6 | 889 7 | 3842 8 | 1481 9 | 1006 10 | 648 11 | 3420 12 | 3684 13 | 827 14 | 3510 15 | 292 16 | 269 17 | 2336 18 | 1945 19 | 2884 20 | 4102 21 | 1883 22 | 2397 23 | 2881 24 | 3237 25 | 2147 26 | 55 27 | 3574 28 | 3345 29 | 1016 30 | 1524 31 | 2934 32 | 1453 33 | 1911 34 | 2768 35 | 2965 36 | 585 37 | 471 38 | 3028 39 | 3445 40 | 1628 41 | 3374 42 | 629 43 | 2002 44 | 3767 45 | 1372 46 | 320 47 | 2851 48 | 1150 49 | 2956 50 | 559 51 | 69 52 | 2875 53 | 708 54 | 20 55 | 3688 56 | 4019 57 | 3962 58 | 1172 59 | 2759 60 | 3432 61 | 434 62 | 785 63 | 3871 64 | 2269 65 | 1191 66 | 1477 67 | 254 68 | 19 69 | 2270 70 | 892 71 | 2602 72 | 945 73 | 3457 74 | 4093 75 | 420 76 | 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172 | 2523 173 | 3303 174 | 2855 175 | 2314 176 | 1249 177 | 677 178 | 4036 179 | 621 180 | 1893 181 | 4086 182 | 811 183 | 1438 184 | 2659 185 | 1658 186 | 3512 187 | 1357 188 | 3531 189 | 1979 190 | 3855 191 | 1441 192 | 949 193 | 1097 194 | 2766 195 | 2824 196 | 2963 197 | 3408 198 | 1033 199 | 798 200 | 3920 201 | 181 202 | 3305 203 | 1829 204 | 495 205 | 2366 206 | 1168 207 | 1917 208 | 2610 209 | 3589 210 | 3732 211 | 1449 212 | 410 213 | 3520 214 | 285 215 | 3527 216 | 1599 217 | 3053 218 | 2353 219 | 1275 220 | 342 221 | 1761 222 | 650 223 | 452 224 | 2711 225 | 408 226 | 1684 227 | 2404 228 | 1507 229 | 2854 230 | 3200 231 | 120 232 | 841 233 | 3070 234 | 1500 235 | 2786 236 | 2435 237 | 3023 238 | 2861 239 | 595 240 | 769 241 | 317 242 | 2710 243 | 698 244 | 3160 245 | 119 246 | 182 247 | 2272 248 | 425 249 | 859 250 | 580 251 | 3733 252 | 2791 253 | 2613 254 | 2179 255 | 883 256 | 3251 257 | 2107 258 | 546 259 | 3808 260 | 2016 261 | 2316 262 | 3411 263 | 2879 264 | 4007 265 | 3686 266 | 549 267 | 341 268 | 1804 269 | 3015 270 | 169 271 | 2384 272 | 718 273 | 2537 274 | 1770 275 | 1242 276 | 2045 277 | 641 278 | 3675 279 | 1581 280 | 1589 281 | 1520 282 | 2134 283 | 1929 284 | 3640 285 | 1490 286 | 381 287 | 1638 288 | 398 289 | 3299 290 | 1677 291 | 2220 292 | 3127 293 | 3588 294 | 4030 295 | 2865 296 | 2858 297 | 29 298 | 1887 299 | 2932 300 | 3618 301 | 1143 302 | 2369 303 | 2237 304 | 997 305 | 1952 306 | 1810 307 | 3109 308 | 1226 309 | 1571 310 | 137 311 | 3163 312 | 3935 313 | 846 314 | 126 315 | 508 316 | 2438 317 | 73 318 | 2547 319 | 295 320 | 3275 321 | 3968 322 | 2723 323 | 2951 324 | 3268 325 | 735 326 | 3122 327 | 2139 328 | 4 329 | 4085 330 | 806 331 | 1220 332 | 1992 333 | 3418 334 | 3689 335 | 2828 336 | 2764 337 | 389 338 | 2430 339 | 1973 340 | 2028 341 | 3066 342 | 1643 343 | 1141 344 | 3290 345 | 4094 346 | 319 347 | 3586 348 | 2464 349 | 502 350 | 2114 351 | 1906 352 | 1109 353 | 2041 354 | 2275 355 | 1691 356 | 1859 357 | 9 358 | 451 359 | 3915 360 | 2581 361 | 2242 362 | 1519 363 | 2803 364 | 3112 365 | 1099 366 | 2225 367 | 1548 368 | 703 369 | 1020 370 | 3717 371 | 2754 372 | 3874 373 | 2054 374 | 1871 375 | 1314 376 | 1700 377 | 3256 378 | 932 379 | 1486 380 | 1204 381 | 3758 382 | 1933 383 | 3605 384 | 510 385 | 3992 386 | 2120 387 | 2698 388 | 1999 389 | 751 390 | 2413 391 | 2982 392 | 310 393 | 3509 394 | 2073 395 | 2359 396 | 2767 397 | 2370 398 | 123 399 | 3561 400 | 10 401 | 1769 402 | 59 403 | 3207 404 | 1888 405 | 3008 406 | 2605 407 | 752 408 | 2896 409 | 2834 410 | 2757 411 | 1443 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-8.txt: -------------------------------------------------------------------------------- 1 | 178 2 | 3696 3 | 4090 4 | 2157 5 | 1818 6 | 3458 7 | 1988 8 | 1478 9 | 3252 10 | 343 11 | 1981 12 | 2691 13 | 3179 14 | 3996 15 | 424 16 | 572 17 | 3849 18 | 3905 19 | 50 20 | 2580 21 | 276 22 | 1277 23 | 1337 24 | 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124 | 3680 125 | 3974 126 | 3757 127 | 1356 128 | 3987 129 | 3572 130 | 109 131 | 2200 132 | 1701 133 | 25 134 | 1615 135 | 1199 136 | 2961 137 | 2520 138 | 1059 139 | 3858 140 | 3288 141 | 1538 142 | 1468 143 | 225 144 | 143 145 | 249 146 | 1814 147 | 3743 148 | 2334 149 | 2994 150 | 3983 151 | 844 152 | 2412 153 | 3245 154 | 3552 155 | 1210 156 | 3626 157 | 509 158 | 2988 159 | 2335 160 | 3464 161 | 2263 162 | 3883 163 | 2326 164 | 951 165 | 1790 166 | 2451 167 | 1737 168 | 3 169 | 344 170 | 102 171 | 665 172 | 1941 173 | 3936 174 | 1540 175 | 3700 176 | 2530 177 | 2095 178 | 3827 179 | 2 180 | 3922 181 | 1953 182 | 2149 183 | 3171 184 | 2771 185 | 3176 186 | 1565 187 | 1602 188 | 1228 189 | 1013 190 | 332 191 | 1193 192 | 3181 193 | 492 194 | 3330 195 | 2755 196 | 374 197 | 2701 198 | 719 199 | 1385 200 | 167 201 | 1891 202 | 2219 203 | 1858 204 | 368 205 | 3124 206 | 3623 207 | 3273 208 | 2151 209 | 18 210 | 1671 211 | 2967 212 | 2527 213 | 3699 214 | 487 215 | 2783 216 | 460 217 | 1573 218 | 1015 219 | 529 220 | 2618 221 | 964 222 | 1824 223 | 2398 224 | 1717 225 | 1440 226 | 454 227 | 1543 228 | 288 229 | 923 230 | 1670 231 | 2886 232 | 4105 233 | 2354 234 | 941 235 | 2940 236 | 2948 237 | 3775 238 | 3825 239 | 2603 240 | 3486 241 | 1266 242 | 649 243 | 836 244 | 3253 245 | 3077 246 | 3860 247 | 3910 248 | 1659 249 | 3924 250 | 2740 251 | 738 252 | 2441 253 | 4109 254 | 1976 255 | 2158 256 | 590 257 | 57 258 | 2916 259 | 1853 260 | 1235 261 | 801 262 | 2654 263 | 2168 264 | 3169 265 | 404 266 | 2395 267 | 3739 268 | 1108 269 | 2673 270 | 668 271 | 1661 272 | 631 273 | 2373 274 | 2885 275 | 3394 276 | 51 277 | 1673 278 | 2360 279 | 1349 280 | 2223 281 | 2579 282 | 1959 283 | 71 284 | 49 285 | 1395 286 | 1133 287 | 1194 288 | 1927 289 | 484 290 | 53 291 | 2888 292 | 4072 293 | 3367 294 | 3272 295 | 1510 296 | 2555 297 | 824 298 | 3467 299 | 1731 300 | 2952 301 | 2575 302 | 1554 303 | 1084 304 | 3413 305 | 3281 306 | 1931 307 | 1265 308 | 1938 309 | 1330 310 | 1765 311 | 1390 312 | 2062 313 | 334 314 | 747 315 | 2462 316 | 1323 317 | 3159 318 | 3029 319 | 611 320 | 2221 321 | 3079 322 | 4063 323 | 2734 324 | 1326 325 | 2750 326 | 2453 327 | 2365 328 | 2025 329 | 606 330 | 2911 331 | 2391 332 | 145 333 | 938 334 | 915 335 | 1404 336 | 1861 337 | 2203 338 | 3914 339 | 2461 340 | 2471 341 | 1675 342 | 3695 343 | 3146 344 | 3317 345 | 2477 346 | 3157 347 | 789 348 | 2686 349 | 1076 350 | 838 351 | 2629 352 | 3383 353 | 3529 354 | 2026 355 | 2386 356 | 4031 357 | 268 358 | 1333 359 | 1514 360 | 634 361 | 467 362 | 1255 363 | 2061 364 | 3629 365 | 912 366 | 2234 367 | 2261 368 | 2040 369 | 1991 370 | 1561 371 | 2385 372 | 2006 373 | 761 374 | 235 375 | 885 376 | 3751 377 | 347 378 | 1054 379 | 3254 380 | 3604 381 | 1082 382 | 2000 383 | 3664 384 | 2919 385 | 1445 386 | 896 387 | 1455 388 | 478 389 | 3665 390 | 1648 391 | 758 392 | 2657 393 | 163 394 | 3760 395 | 2976 396 | 767 397 | 714 398 | 461 399 | 2930 400 | 1365 401 | 1568 402 | 2291 403 | 2636 404 | 3412 405 | 3786 406 | 3725 407 | 2253 408 | 2185 409 | 4005 410 | 2445 411 | 3995 412 | -------------------------------------------------------------------------------- /dataset/NCI1/10fold_idx/test_idx-9.txt: -------------------------------------------------------------------------------- 1 | 2241 2 | 1847 3 | 2808 4 | 1896 5 | 3473 6 | 217 7 | 2957 8 | 3103 9 | 724 10 | 1908 11 | 2565 12 | 1459 13 | 2545 14 | 3384 15 | 3613 16 | 1032 17 | 1442 18 | 825 19 | 144 20 | 3353 21 | 2781 22 | 3507 23 | 578 24 | 42 25 | 842 26 | 2901 27 | 2614 28 | 115 29 | 2751 30 | 1131 31 | 1177 32 | 366 33 | 2591 34 | 2920 35 | 2486 36 | 2361 37 | 3174 38 | 569 39 | 2401 40 | 1987 41 | 1294 42 | 660 43 | 1960 44 | 1845 45 | 1583 46 | 1689 47 | 1974 48 | 2320 49 | 1371 50 | 3841 51 | 3969 52 | 784 53 | 2357 54 | 530 55 | 847 56 | 880 57 | 1773 58 | 1362 59 | 1264 60 | 2526 61 | 128 62 | 985 63 | 2193 64 | 1916 65 | 759 66 | 975 67 | 3182 68 | 3876 69 | 3643 70 | 3728 71 | 3649 72 | 1667 73 | 3597 74 | 3135 75 | 158 76 | 3814 77 | 2483 78 | 2019 79 | 1475 80 | 4048 81 | 1289 82 | 2084 83 | 220 84 | 2653 85 | 770 86 | 2914 87 | 3039 88 | 3912 89 | 1188 90 | 598 91 | 1236 92 | 2287 93 | 687 94 | 1491 95 | 3044 96 | 160 97 | 3761 98 | 3074 99 | 821 100 | 1840 101 | 4104 102 | 2383 103 | 2487 104 | 1609 105 | 1106 106 | 474 107 | 900 108 | 839 109 | 173 110 | 3637 111 | 2569 112 | 763 113 | 3243 114 | 876 115 | 3569 116 | 2143 117 | 2923 118 | 2024 119 | 2154 120 | 3221 121 | 976 122 | 2379 123 | 3867 124 | 377 125 | 3835 126 | 1200 127 | 238 128 | 1444 129 | 3764 130 | 324 131 | 3941 132 | 1570 133 | 3939 134 | 2642 135 | 449 136 | 336 137 | 3398 138 | 3826 139 | 3873 140 | 1446 141 | 2890 142 | 2568 143 | 168 144 | 2592 145 | 982 146 | 1872 147 | 2993 148 | 867 149 | 4066 150 | 1154 151 | 1174 152 | 3397 153 | 2007 154 | 2121 155 | 1640 156 | 1912 157 | 3389 158 | 3385 159 | 843 160 | 1668 161 | 4051 162 | 3648 163 | 2077 164 | 3186 165 | 2725 166 | 3781 167 | 3746 168 | 2588 169 | 3150 170 | 3493 171 | 907 172 | 1821 173 | 2666 174 | 3726 175 | 2161 176 | 865 177 | 1566 178 | 38 179 | 3024 180 | 1112 181 | 3175 182 | 2482 183 | 2752 184 | 3896 185 | 2105 186 | 835 187 | 130 188 | 211 189 | 854 190 | 327 191 | 1135 192 | 756 193 | 2562 194 | 2447 195 | 1462 196 | 248 197 | 1290 198 | 2507 199 | 3952 200 | 393 201 | 605 202 | 683 203 | 1827 204 | 3097 205 | 562 206 | 278 207 | 1509 208 | 837 209 | 1943 210 | 3614 211 | 3451 212 | 826 213 | 1782 214 | 3838 215 | 579 216 | 1954 217 | 2887 218 | 577 219 | 83 220 | 2902 221 | 361 222 | 1542 223 | 2212 224 | 246 225 | 2925 226 | 2557 227 | 2928 228 | 250 229 | 274 230 | 3756 231 | 2795 232 | 2662 233 | 2677 234 | 2230 235 | 2355 236 | 3325 237 | 376 238 | 1303 239 | 114 240 | 863 241 | 3406 242 | 2124 243 | 2329 244 | 1184 245 | 2127 246 | 700 247 | 2931 248 | 2030 249 | 2286 250 | 3518 251 | 3466 252 | 2731 253 | 1167 254 | 2341 255 | 2460 256 | 3462 257 | 3041 258 | 1368 259 | 1113 260 | 3708 261 | 2655 262 | 2640 263 | 1308 264 | 3107 265 | 3289 266 | 1751 267 | 3661 268 | 3677 269 | 2091 270 | 1063 271 | 3925 272 | 2665 273 | 803 274 | 3565 275 | 3072 276 | 3797 277 | 3223 278 | 233 279 | 3718 280 | 588 281 | 3089 282 | 27 283 | 2141 284 | 776 285 | 1343 286 | 2623 287 | 2715 288 | 1074 289 | 3816 290 | 3236 291 | 37 292 | 187 293 | 2700 294 | 1119 295 | 1325 296 | 2458 297 | 2029 298 | 4064 299 | 1321 300 | 1240 301 | 1811 302 | 3055 303 | 2872 304 | 387 305 | 3887 306 | 1311 307 | 2333 308 | 948 309 | 2323 310 | 477 311 | 2087 312 | 3620 313 | 2817 314 | 2658 315 | 3727 316 | 3371 317 | 1296 318 | 1429 319 | 2164 320 | 131 321 | 1417 322 | 200 323 | 3013 324 | 1205 325 | 330 326 | 3497 327 | 3058 328 | 261 329 | 1539 330 | 125 331 | 3388 332 | 1968 333 | 2549 334 | 674 335 | 2912 336 | 3539 337 | 447 338 | 219 339 | 1310 340 | 2338 341 | 3162 342 | 2596 343 | 2929 344 | 1457 345 | 2622 346 | 1772 347 | 3820 348 | 1595 349 | 3710 350 | 36 351 | 3692 352 | 642 353 | 512 354 | 2933 355 | 3065 356 | 1257 357 | 2644 358 | 1771 359 | 2167 360 | 1225 361 | 2012 362 | 1993 363 | 886 364 | 392 365 | 2196 366 | 1969 367 | 2788 368 | 3027 369 | 1666 370 | 1146 371 | 3401 372 | 3514 373 | 3810 374 | 3713 375 | 1781 376 | 1081 377 | 3400 378 | 597 379 | 95 380 | 2633 381 | 1680 382 | 3478 383 | 4032 384 | 2399 385 | 929 386 | 1499 387 | 3297 388 | 1160 389 | 1807 390 | 946 391 | 961 392 | 1281 393 | 1313 394 | 2977 395 | 3693 396 | 3022 397 | 2793 398 | 65 399 | 1738 400 | 2181 401 | 962 402 | 781 403 | 2744 404 | 3999 405 | 1309 406 | 3697 407 | 2748 408 | 1806 409 | 3280 410 | 1766 411 | 1555 412 | -------------------------------------------------------------------------------- /dataset/NCI1/NCI1.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joisino/random-features/180e926edd601e10d4b115a0a1722fa7183f7f13/dataset/NCI1/NCI1.mat -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-1.txt: -------------------------------------------------------------------------------- 1 | 893 2 | 828 3 | 826 4 | 774 5 | 850 6 | 183 7 | 916 8 | 414 9 | 83 10 | 954 11 | 684 12 | 57 13 | 749 14 | 1103 15 | 365 16 | 658 17 | 527 18 | 762 19 | 282 20 | 582 21 | 343 22 | 243 23 | 922 24 | 320 25 | 10 26 | 596 27 | 845 28 | 358 29 | 3 30 | 489 31 | 1034 32 | 871 33 | 271 34 | 171 35 | 1070 36 | 375 37 | 518 38 | 418 39 | 439 40 | 337 41 | 553 42 | 69 43 | 544 44 | 498 45 | 84 46 | 880 47 | 746 48 | 107 49 | 1094 50 | 55 51 | 1035 52 | 16 53 | 451 54 | 551 55 | 759 56 | 760 57 | 34 58 | 926 59 | 936 60 | 176 61 | 310 62 | 679 63 | 989 64 | 1015 65 | 960 66 | 95 67 | 67 68 | 294 69 | 700 70 | 363 71 | 842 72 | 559 73 | 698 74 | 53 75 | 611 76 | 108 77 | 1064 78 | 216 79 | 824 80 | 1047 81 | 205 82 | 751 83 | 74 84 | 399 85 | 785 86 | 528 87 | 680 88 | 127 89 | 135 90 | 814 91 | 360 92 | 334 93 | 109 94 | 1011 95 | 73 96 | 986 97 | 975 98 | 149 99 | 952 100 | 909 101 | 182 102 | 60 103 | 541 104 | 686 105 | 768 106 | 259 107 | 192 108 | 438 109 | 434 110 | 31 111 | 145 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-10.txt: -------------------------------------------------------------------------------- 1 | 437 2 | 589 3 | 645 4 | 58 5 | 710 6 | 20 7 | 370 8 | 634 9 | 631 10 | 835 11 | 1098 12 | 870 13 | 76 14 | 39 15 | 447 16 | 920 17 | 769 18 | 1032 19 | 865 20 | 1066 21 | 864 22 | 344 23 | 978 24 | 1008 25 | 42 26 | 627 27 | 206 28 | 777 29 | 461 30 | 287 31 | 416 32 | 560 33 | 283 34 | 331 35 | 460 36 | 542 37 | 707 38 | 285 39 | 944 40 | 607 41 | 614 42 | 163 43 | 431 44 | 1082 45 | 887 46 | 248 47 | 465 48 | 601 49 | 789 50 | 4 51 | 836 52 | 364 53 | 585 54 | 378 55 | 792 56 | 111 57 | 592 58 | 739 59 | 382 60 | 919 61 | 1071 62 | 1014 63 | 308 64 | 674 65 | 268 66 | 488 67 | 846 68 | 886 69 | 247 70 | 971 71 | 329 72 | 848 73 | 940 74 | 1079 75 | 1090 76 | 517 77 | 1092 78 | 156 79 | 719 80 | 125 81 | 965 82 | 403 83 | 925 84 | 619 85 | 1003 86 | 484 87 | 519 88 | 690 89 | 529 90 | 1046 91 | 475 92 | 937 93 | 586 94 | 219 95 | 381 96 | 564 97 | 357 98 | 778 99 | 212 100 | 620 101 | 384 102 | 22 103 | 985 104 | 362 105 | 1057 106 | 1077 107 | 1053 108 | 64 109 | 1089 110 | 401 111 | 692 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-2.txt: -------------------------------------------------------------------------------- 1 | 781 2 | 449 3 | 63 4 | 43 5 | 100 6 | 151 7 | 255 8 | 376 9 | 432 10 | 770 11 | 188 12 | 78 13 | 742 14 | 1000 15 | 162 16 | 999 17 | 115 18 | 152 19 | 804 20 | 1093 21 | 935 22 | 662 23 | 116 24 | 21 25 | 819 26 | 274 27 | 539 28 | 533 29 | 756 30 | 640 31 | 906 32 | 466 33 | 242 34 | 180 35 | 409 36 | 190 37 | 101 38 | 181 39 | 950 40 | 113 41 | 581 42 | 1026 43 | 2 44 | 50 45 | 191 46 | 678 47 | 458 48 | 506 49 | 372 50 | 508 51 | 833 52 | 575 53 | 812 54 | 193 55 | 319 56 | 830 57 | 1105 58 | 772 59 | 687 60 | 1030 61 | 910 62 | 630 63 | 143 64 | 947 65 | 537 66 | 761 67 | 267 68 | 885 69 | 222 70 | 726 71 | 224 72 | 286 73 | 262 74 | 681 75 | 210 76 | 194 77 | 1110 78 | 1029 79 | 213 80 | 788 81 | 974 82 | 844 83 | 832 84 | 261 85 | 37 86 | 890 87 | 328 88 | 17 89 | 428 90 | 0 91 | 32 92 | 443 93 | 874 94 | 336 95 | 280 96 | 374 97 | 433 98 | 1074 99 | 322 100 | 47 101 | 203 102 | 941 103 | 1054 104 | 164 105 | 876 106 | 892 107 | 829 108 | 520 109 | 708 110 | 1019 111 | 942 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-3.txt: -------------------------------------------------------------------------------- 1 | 436 2 | 1067 3 | 554 4 | 1056 5 | 195 6 | 59 7 | 911 8 | 1112 9 | 716 10 | 633 11 | 137 12 | 134 13 | 609 14 | 1024 15 | 38 16 | 445 17 | 204 18 | 258 19 | 51 20 | 198 21 | 408 22 | 373 23 | 754 24 | 855 25 | 827 26 | 837 27 | 963 28 | 332 29 | 315 30 | 422 31 | 355 32 | 857 33 | 293 34 | 747 35 | 140 36 | 981 37 | 493 38 | 531 39 | 1084 40 | 123 41 | 957 42 | 427 43 | 970 44 | 147 45 | 800 46 | 250 47 | 501 48 | 883 49 | 54 50 | 884 51 | 822 52 | 388 53 | 665 54 | 624 55 | 353 56 | 199 57 | 976 58 | 1010 59 | 281 60 | 915 61 | 1107 62 | 816 63 | 291 64 | 214 65 | 535 66 | 709 67 | 813 68 | 1051 69 | 841 70 | 330 71 | 165 72 | 831 73 | 534 74 | 423 75 | 570 76 | 1001 77 | 780 78 | 170 79 | 946 80 | 882 81 | 352 82 | 642 83 | 1111 84 | 968 85 | 918 86 | 492 87 | 504 88 | 644 89 | 938 90 | 637 91 | 1017 92 | 562 93 | 587 94 | 899 95 | 26 96 | 91 97 | 244 98 | 1006 99 | 154 100 | 81 101 | 146 102 | 229 103 | 389 104 | 6 105 | 66 106 | 817 107 | 68 108 | 234 109 | 49 110 | 806 111 | 584 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-4.txt: -------------------------------------------------------------------------------- 1 | 5 2 | 683 3 | 552 4 | 1095 5 | 61 6 | 574 7 | 410 8 | 1050 9 | 1005 10 | 442 11 | 209 12 | 177 13 | 894 14 | 197 15 | 302 16 | 196 17 | 245 18 | 996 19 | 862 20 | 1025 21 | 987 22 | 877 23 | 473 24 | 682 25 | 664 26 | 276 27 | 653 28 | 485 29 | 1044 30 | 118 31 | 477 32 | 695 33 | 19 34 | 536 35 | 65 36 | 867 37 | 7 38 | 233 39 | 36 40 | 875 41 | 386 42 | 97 43 | 80 44 | 444 45 | 943 46 | 545 47 | 666 48 | 464 49 | 404 50 | 723 51 | 908 52 | 339 53 | 14 54 | 538 55 | 499 56 | 227 57 | 515 58 | 379 59 | 402 60 | 313 61 | 1063 62 | 490 63 | 237 64 | 33 65 | 732 66 | 891 67 | 811 68 | 479 69 | 524 70 | 577 71 | 1040 72 | 510 73 | 128 74 | 799 75 | 133 76 | 1108 77 | 129 78 | 324 79 | 858 80 | 600 81 | 604 82 | 333 83 | 933 84 | 949 85 | 667 86 | 839 87 | 419 88 | 223 89 | 1038 90 | 348 91 | 341 92 | 734 93 | 318 94 | 138 95 | 622 96 | 743 97 | 459 98 | 647 99 | 810 100 | 945 101 | 300 102 | 450 103 | 921 104 | 1021 105 | 825 106 | 703 107 | 670 108 | 169 109 | 1085 110 | 94 111 | 215 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-5.txt: -------------------------------------------------------------------------------- 1 | 948 2 | 671 3 | 470 4 | 1 5 | 278 6 | 543 7 | 676 8 | 807 9 | 220 10 | 218 11 | 861 12 | 532 13 | 1081 14 | 809 15 | 984 16 | 878 17 | 150 18 | 612 19 | 803 20 | 641 21 | 380 22 | 392 23 | 407 24 | 1049 25 | 481 26 | 27 27 | 279 28 | 618 29 | 299 30 | 321 31 | 236 32 | 184 33 | 1059 34 | 240 35 | 818 36 | 729 37 | 718 38 | 241 39 | 815 40 | 173 41 | 696 42 | 737 43 | 847 44 | 913 45 | 453 46 | 1100 47 | 1068 48 | 157 49 | 160 50 | 567 51 | 951 52 | 763 53 | 701 54 | 843 55 | 661 56 | 660 57 | 898 58 | 290 59 | 791 60 | 56 61 | 869 62 | 1020 63 | 805 64 | 309 65 | 15 66 | 597 67 | 958 68 | 516 69 | 927 70 | 632 71 | 500 72 | 1037 73 | 354 74 | 174 75 | 794 76 | 997 77 | 235 78 | 717 79 | 733 80 | 406 81 | 623 82 | 62 83 | 654 84 | 249 85 | 745 86 | 257 87 | 28 88 | 8 89 | 41 90 | 568 91 | 576 92 | 1031 93 | 425 94 | 1106 95 | 1083 96 | 838 97 | 142 98 | 616 99 | 103 100 | 456 101 | 167 102 | 513 103 | 435 104 | 697 105 | 482 106 | 820 107 | 961 108 | 648 109 | 1075 110 | 1061 111 | 753 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-6.txt: -------------------------------------------------------------------------------- 1 | 452 2 | 900 3 | 512 4 | 712 5 | 572 6 | 907 7 | 779 8 | 70 9 | 306 10 | 967 11 | 782 12 | 446 13 | 988 14 | 398 15 | 672 16 | 694 17 | 265 18 | 787 19 | 724 20 | 771 21 | 1076 22 | 766 23 | 599 24 | 35 25 | 840 26 | 1002 27 | 275 28 | 87 29 | 391 30 | 992 31 | 351 32 | 230 33 | 148 34 | 1097 35 | 187 36 | 715 37 | 917 38 | 1041 39 | 872 40 | 796 41 | 77 42 | 368 43 | 1091 44 | 359 45 | 685 46 | 23 47 | 1099 48 | 269 49 | 1101 50 | 897 51 | 563 52 | 548 53 | 277 54 | 668 55 | 879 56 | 593 57 | 476 58 | 102 59 | 139 60 | 396 61 | 1033 62 | 474 63 | 854 64 | 881 65 | 693 66 | 797 67 | 202 68 | 991 69 | 93 70 | 725 71 | 440 72 | 208 73 | 628 74 | 238 75 | 1087 76 | 1073 77 | 12 78 | 773 79 | 583 80 | 569 81 | 46 82 | 669 83 | 727 84 | 959 85 | 691 86 | 1028 87 | 327 88 | 755 89 | 955 90 | 652 91 | 350 92 | 982 93 | 903 94 | 595 95 | 289 96 | 801 97 | 1096 98 | 934 99 | 558 100 | 928 101 | 121 102 | 395 103 | 397 104 | 1016 105 | 1013 106 | 783 107 | 522 108 | 1048 109 | 509 110 | 790 111 | 496 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-7.txt: -------------------------------------------------------------------------------- 1 | 744 2 | 1065 3 | 866 4 | 605 5 | 711 6 | 939 7 | 48 8 | 232 9 | 590 10 | 131 11 | 629 12 | 834 13 | 758 14 | 973 15 | 393 16 | 86 17 | 155 18 | 317 19 | 98 20 | 863 21 | 651 22 | 175 23 | 713 24 | 704 25 | 953 26 | 377 27 | 349 28 | 765 29 | 463 30 | 478 31 | 720 32 | 972 33 | 525 34 | 221 35 | 211 36 | 426 37 | 495 38 | 896 39 | 371 40 | 342 41 | 448 42 | 72 43 | 688 44 | 312 45 | 189 46 | 307 47 | 1009 48 | 335 49 | 117 50 | 141 51 | 298 52 | 962 53 | 853 54 | 905 55 | 598 56 | 468 57 | 573 58 | 9 59 | 931 60 | 1045 61 | 530 62 | 316 63 | 246 64 | 284 65 | 119 66 | 610 67 | 161 68 | 185 69 | 793 70 | 673 71 | 497 72 | 340 73 | 786 74 | 1109 75 | 387 76 | 491 77 | 18 78 | 639 79 | 263 80 | 613 81 | 521 82 | 85 83 | 608 84 | 580 85 | 752 86 | 757 87 | 296 88 | 923 89 | 1027 90 | 912 91 | 424 92 | 638 93 | 172 94 | 659 95 | 523 96 | 40 97 | 383 98 | 1023 99 | 1080 100 | 270 101 | 851 102 | 798 103 | 390 104 | 256 105 | 112 106 | 366 107 | 217 108 | 130 109 | 228 110 | 549 111 | 929 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-8.txt: -------------------------------------------------------------------------------- 1 | 92 2 | 738 3 | 413 4 | 914 5 | 483 6 | 158 7 | 728 8 | 649 9 | 721 10 | 179 11 | 895 12 | 979 13 | 266 14 | 201 15 | 420 16 | 1086 17 | 741 18 | 852 19 | 821 20 | 207 21 | 24 22 | 89 23 | 1004 24 | 494 25 | 1042 26 | 1102 27 | 462 28 | 655 29 | 502 30 | 297 31 | 565 32 | 873 33 | 643 34 | 930 35 | 400 36 | 722 37 | 555 38 | 657 39 | 556 40 | 675 41 | 114 42 | 186 43 | 338 44 | 980 45 | 990 46 | 323 47 | 411 48 | 932 49 | 889 50 | 594 51 | 1060 52 | 421 53 | 88 54 | 467 55 | 505 56 | 731 57 | 326 58 | 736 59 | 106 60 | 124 61 | 546 62 | 45 63 | 487 64 | 122 65 | 253 66 | 99 67 | 239 68 | 254 69 | 44 70 | 346 71 | 82 72 | 969 73 | 904 74 | 273 75 | 571 76 | 120 77 | 1069 78 | 471 79 | 394 80 | 292 81 | 998 82 | 480 83 | 1088 84 | 617 85 | 159 86 | 225 87 | 1052 88 | 252 89 | 71 90 | 901 91 | 902 92 | 849 93 | 635 94 | 784 95 | 1072 96 | 251 97 | 29 98 | 775 99 | 385 100 | 430 101 | 295 102 | 168 103 | 417 104 | 136 105 | 856 106 | 441 107 | 178 108 | 735 109 | 75 110 | 96 111 | 579 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/test_idx-9.txt: -------------------------------------------------------------------------------- 1 | 1055 2 | 90 3 | 144 4 | 412 5 | 367 6 | 740 7 | 625 8 | 153 9 | 823 10 | 578 11 | 995 12 | 304 13 | 132 14 | 1039 15 | 956 16 | 288 17 | 547 18 | 166 19 | 110 20 | 105 21 | 561 22 | 345 23 | 588 24 | 1104 25 | 361 26 | 1058 27 | 356 28 | 1036 29 | 511 30 | 1062 31 | 272 32 | 983 33 | 615 34 | 526 35 | 455 36 | 347 37 | 303 38 | 663 39 | 301 40 | 30 41 | 705 42 | 888 43 | 714 44 | 260 45 | 79 46 | 1022 47 | 646 48 | 702 49 | 1018 50 | 602 51 | 429 52 | 993 53 | 677 54 | 764 55 | 231 56 | 469 57 | 808 58 | 748 59 | 650 60 | 472 61 | 730 62 | 507 63 | 964 64 | 924 65 | 486 66 | 1012 67 | 566 68 | 795 69 | 325 70 | 11 71 | 415 72 | 966 73 | 776 74 | 603 75 | 1007 76 | 305 77 | 868 78 | 13 79 | 1078 80 | 503 81 | 860 82 | 977 83 | 994 84 | 689 85 | 750 86 | 767 87 | 621 88 | 311 89 | 802 90 | 126 91 | 226 92 | 1043 93 | 606 94 | 514 95 | 557 96 | 636 97 | 626 98 | 591 99 | 540 100 | 656 101 | 369 102 | 706 103 | 405 104 | 699 105 | 104 106 | 859 107 | 200 108 | 550 109 | 264 110 | 457 111 | 52 112 | -------------------------------------------------------------------------------- /dataset/PROTEINS/10fold_idx/train_idx-1.txt: -------------------------------------------------------------------------------- 1 | 781 2 | 449 3 | 63 4 | 43 5 | 100 6 | 151 7 | 255 8 | 376 9 | 432 10 | 770 11 | 188 12 | 78 13 | 742 14 | 1000 15 | 162 16 | 999 17 | 115 18 | 152 19 | 804 20 | 1093 21 | 935 22 | 662 23 | 116 24 | 21 25 | 819 26 | 274 27 | 539 28 | 533 29 | 756 30 | 640 31 | 906 32 | 466 33 | 242 34 | 180 35 | 409 36 | 190 37 | 101 38 | 181 39 | 950 40 | 113 41 | 581 42 | 1026 43 | 2 44 | 50 45 | 191 46 | 678 47 | 458 48 | 506 49 | 372 50 | 508 51 | 833 52 | 575 53 | 812 54 | 193 55 | 319 56 | 830 57 | 1105 58 | 772 59 | 687 60 | 1030 61 | 910 62 | 630 63 | 143 64 | 947 65 | 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| 739 947 | 382 948 | 919 949 | 1071 950 | 1014 951 | 308 952 | 674 953 | 268 954 | 488 955 | 846 956 | 886 957 | 247 958 | 971 959 | 329 960 | 848 961 | 940 962 | 1079 963 | 1090 964 | 517 965 | 1092 966 | 156 967 | 719 968 | 125 969 | 965 970 | 403 971 | 925 972 | 619 973 | 1003 974 | 484 975 | 519 976 | 690 977 | 529 978 | 1046 979 | 475 980 | 937 981 | 586 982 | 219 983 | 381 984 | 564 985 | 357 986 | 778 987 | 212 988 | 620 989 | 384 990 | 22 991 | 985 992 | 362 993 | 1057 994 | 1077 995 | 1053 996 | 64 997 | 1089 998 | 401 999 | 692 1000 | 314 1001 | 454 1002 | 25 1003 | -------------------------------------------------------------------------------- /dataset/PROTEINS/PROTEINS.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/joisino/random-features/180e926edd601e10d4b115a0a1722fa7183f7f13/dataset/PROTEINS/PROTEINS.mat -------------------------------------------------------------------------------- /dataset_gen.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import os 4 | import sys 5 | 6 | import networkx as nx 7 | import numpy as np 8 | 9 | 10 | def local_cluster_coefficient(G): 11 | label = [0 for i in range(len(G))] 12 | for i in range(len(G)): 13 | for j in G[i]: 14 | for k in G[i]: 15 | if k <= j: 16 | continue 17 | if j in G[k]: 18 | label[i] += 1 19 | return label 20 | 21 | 22 | def tri_check(G): 23 | label = [0 for i in range(len(G))] 24 | for i in range(len(G)): 25 | for j in G[i]: 26 | for k in G[i]: 27 | if j in G[k]: 28 | label[i] = 1 29 | return label 30 | 31 | 32 | def main(): 33 | deg = 3 34 | num_graphs = 1000 35 | ty = sys.argv[1] 36 | if ty in ['TRIANGLE_EX' or 'LCC_EX' or 'MDS_EX']: 37 | sps = [('train', 20), ('test', 100)] 38 | else: 39 | sps = [('train', 20), ('test', 20)] 40 | for sp in sps: 41 | n = sp[1] 42 | Gs = [] 43 | for _ in range(num_graphs): 44 | g = nx.random_degree_sequence_graph([deg for i in range(n)]) 45 | G = [[] for i in range(n)] 46 | for e in g.edges: 47 | G[e[0]].append(e[1]) 48 | G[e[1]].append(e[0]) 49 | if ty in ['TRIANGLE', 'TRIANGLE_EX']: 50 | l = tri_check(G) 51 | elif ty in ['LCC', 'LCC_EX']: 52 | l = local_cluster_coefficient(G) 53 | else: 54 | l = [0 for i in range(n)] 55 | Gs.append((G, l)) 56 | 57 | basedir = f'dataset/{ty}' 58 | if not os.path.exists(basedir): 59 | os.makedirs(basedir) 60 | with open(f'{basedir}/{ty}_{sp[0]}.txt', 'w') as f: 61 | f.write(f'{num_graphs}\n') 62 | for i in range(num_graphs): 63 | f.write(f'{n} 0\n') 64 | G, l = Gs[i] 65 | for j in range(n): 66 | f.write(f'{l[j]} {deg}') 67 | for k in G[j]: 68 | f.write(f' {k}') 69 | f.write('\n') 70 | 71 | if __name__ == '__main__': 72 | main() 73 | -------------------------------------------------------------------------------- /exec.sh: -------------------------------------------------------------------------------- 1 | 2 | datasets_syn=( 3 | "TRIANGLE" 4 | "TRIANGLE_EX" 5 | "LCC" 6 | "LCC_EX" 7 | ) 8 | 9 | mds=( 10 | "MDS" 11 | "MDS_EX" 12 | ) 13 | 14 | datasets_real=( 15 | "MUTAG" 16 | "NCI1" 17 | "PROTEINS" 18 | ) 19 | 20 | 21 | mkdir -p logs 22 | mkdir -p dataset 23 | 24 | for i in "${datasets_syn[@]}"; do 25 | python3 dataset_gen.py $i 26 | python3 main.py --dataset $i --random 100 --filename logs/log_${i}_random 27 | python3 main.py --dataset $i --filename logs/log_${i} 28 | python3 main.py --dataset $i --random 100 --filename logs/log_GCN_${i}_random --neighbor_pooling_type average --num_mlp_layers 1 29 | python3 main.py --dataset $i --filename logs/log_GCN_${i} --neighbor_pooling_type average --num_mlp_layers 1 30 | done 31 | 32 | for i in "${mds[@]}"; do 33 | python3 dataset_gen.py $i 34 | python3 main.py --dataset $i --hidden_dim 1024 --num_layers 10 --lr 0.1 --opt sgd --epochs 50000 --random 100 --filename logs/log_${i}_random 35 | python3 main.py --dataset $i --hidden_dim 1024 --num_layers 10 --lr 0.1 --opt sgd --epochs 50000 --filename logs/log_${i} 36 | python3 main.py --dataset $i --hidden_dim 1024 --num_layers 10 --lr 0.1 --opt sgd --epochs 50000 --random 100 --filename logs/log_GCN_${i}_random --neighbor_pooling_type average --num_mlp_layers 1 37 | python3 main.py --dataset $i --hidden_dim 1024 --num_layers 10 --lr 0.1 --opt sgd --epochs 50000 --filename logs/log_GCN_${i} --neighbor_pooling_type average --num_mlp_layers 1 38 | done 39 | 40 | 41 | for i in "${datasets_real[@]}"; do 42 | for j in `seq 0 9`; do 43 | python3 main.py --dataset $i --fold_idx $j --random 100 --filename logs/log_${i}_random 44 | python3 main.py --dataset $i --fold_idx $j --filename logs/log_${i} 45 | python3 main.py --dataset $i --fold_idx $j --random 100 --filename logs/log_GCN_${i}_random --neighbor_pooling_type average --num_mlp_layers 1 46 | python3 main.py --dataset $i --fold_idx $j --filename logs/log_GCN_${i} --neighbor_pooling_type average --num_mlp_layers 1 47 | done 48 | done 49 | 50 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | import torch.optim as optim 6 | import numpy as np 7 | 8 | from tqdm import tqdm 9 | 10 | from util import load_data, separate_data 11 | from models.graphcnn import GraphCNN 12 | 13 | from sklearn.metrics import roc_auc_score 14 | 15 | from algorithms import MDS_LOCAL 16 | 17 | def train(args, model, device, train_graphs, optimizer, criterion, get_labels, epoch): 18 | model.train() 19 | 20 | total_iters = args.iters_per_epoch 21 | pbar = tqdm(range(total_iters), unit='batch') 22 | 23 | loss_accum = 0 24 | for pos in pbar: 25 | selected_idx = np.random.permutation(len(train_graphs))[:args.batch_size] 26 | 27 | batch_graph = [train_graphs[idx] for idx in selected_idx] 28 | output = model(batch_graph) 29 | 30 | labels = get_labels(batch_graph, model).to(device) 31 | 32 | #compute loss 33 | loss = criterion(output, labels) 34 | 35 | #backprop 36 | if optimizer is not None: 37 | optimizer.zero_grad() 38 | loss.backward() 39 | optimizer.step() 40 | 41 | loss = loss.detach().cpu().numpy() 42 | loss_accum += loss 43 | 44 | #report 45 | pbar.set_description('epoch: %d' % (epoch)) 46 | 47 | average_loss = loss_accum/total_iters 48 | print("loss training: %f" % (average_loss)) 49 | 50 | return average_loss 51 | 52 | ###pass data to model with minibatch during testing to avoid memory overflow (does not perform backpropagation) 53 | def pass_data_iteratively(model, graphs, get_labels, minibatch_size=64): 54 | model.eval() 55 | output = [] 56 | labels = torch.zeros(0).long() 57 | idx = np.arange(len(graphs)) 58 | for i in range(0, len(graphs), minibatch_size): 59 | sampled_idx = idx[i:i+minibatch_size] 60 | if len(sampled_idx) == 0: 61 | continue 62 | output.append(model([graphs[j] for j in sampled_idx]).detach()) 63 | labels = torch.cat([labels, get_labels([graphs[j] for j in sampled_idx], model)], dim=0) 64 | return torch.cat(output, 0), labels 65 | 66 | def test(args, model, device, train_graphs, test_graphs, num_classes, get_labels, epoch): 67 | model.eval() 68 | 69 | output, labels = pass_data_iteratively(model, train_graphs, get_labels) 70 | labels = labels.to(device) 71 | acc_train = 0 72 | for i in range(num_classes): 73 | acc_train += roc_auc_score((labels == i).long().cpu().numpy(), output[:, i].cpu().numpy()) / num_classes 74 | 75 | output, labels = pass_data_iteratively(model, test_graphs, get_labels) 76 | labels = labels.to(device) 77 | acc_test = 0 78 | for i in range(num_classes): 79 | acc_test += roc_auc_score((labels == i).long().cpu().numpy(), output[:, i].cpu().numpy()) / num_classes 80 | 81 | return acc_train, acc_test 82 | 83 | 84 | def main(): 85 | # Training settings 86 | # Note: Hyper-parameters need to be tuned in order to obtain results reported in the paper. 87 | parser = argparse.ArgumentParser(description='PyTorch graph convolutional neural net for whole-graph classification') 88 | parser.add_argument('--dataset', type=str, default="MUTAG", 89 | help='name of dataset (default: MUTAG)') 90 | parser.add_argument('--device', type=int, default=0, 91 | help='which gpu to use if any (default: 0)') 92 | parser.add_argument('--batch_size', type=int, default=32, 93 | help='input batch size for training (default: 32)') 94 | parser.add_argument('--iters_per_epoch', type=int, default=50, 95 | help='number of iterations per each epoch (default: 50)') 96 | parser.add_argument('--epochs', type=int, default=350, 97 | help='number of epochs to train (default: 350)') 98 | parser.add_argument('--lr', type=float, default=0.01, 99 | help='learning rate (default: 0.01)') 100 | parser.add_argument('--seed', type=int, default=0, 101 | help='random seed for splitting the dataset into 10 (default: 0)') 102 | parser.add_argument('--fold_idx', type=int, default=0, 103 | help='the index of fold in 10-fold validation. Should be less then 10.') 104 | parser.add_argument('--num_layers', type=int, default=5, 105 | help='number of layers INCLUDING the input one (default: 5)') 106 | parser.add_argument('--num_mlp_layers', type=int, default=2, 107 | help='number of layers for MLP EXCLUDING the input one (default: 2). 1 means linear model.') 108 | parser.add_argument('--hidden_dim', type=int, default=64, 109 | help='number of hidden units (default: 64)') 110 | parser.add_argument('--final_dropout', type=float, default=0.5, 111 | help='final layer dropout (default: 0.5)') 112 | parser.add_argument('--graph_pooling_type', type=str, default="sum", choices=["sum", "average"], 113 | help='Pooling for over nodes in a graph: sum or average') 114 | parser.add_argument('--neighbor_pooling_type', type=str, default="sum", choices=["sum", "average", "max"], 115 | help='Pooling for over neighboring nodes: sum, average or max') 116 | parser.add_argument('--opt', type=str, default="adam", choices=["adam", "sgd"]) 117 | parser.add_argument('--learn_eps', action="store_true", 118 | help='Whether to learn the epsilon weighting for the center nodes. Does not affect training accuracy though.') 119 | parser.add_argument('--degree_as_tag', action="store_true", 120 | help='let the input node features be the degree of nodes (heuristics for unlabeled graph)') 121 | parser.add_argument('--filename', type = str, default = "", 122 | help='output file') 123 | parser.add_argument('--random', type=int, default=None, 124 | help='the range of random features (default: None). None means it does not add random features.') 125 | args = parser.parse_args() 126 | 127 | #set up seeds and gpu device 128 | torch.manual_seed(0) 129 | np.random.seed(0) 130 | device = torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu") 131 | if torch.cuda.is_available(): 132 | torch.cuda.manual_seed_all(0) 133 | 134 | if args.dataset in ['TRIANGLE', 'TRIANGLE_EX', 'LCC', 'LCC_EX', 'MDS', 'MDS_EX']: 135 | node_classification = True 136 | train_graphs, _ = load_data(f'dataset/{args.dataset}/{args.dataset}_train.txt', args.degree_as_tag) 137 | test_graphs, _ = load_data(f'dataset/{args.dataset}/{args.dataset}_test.txt', args.degree_as_tag) 138 | for g in train_graphs + test_graphs: 139 | if args.random: 140 | g.node_features = torch.ones(g.node_features.shape[0], 0) 141 | else: 142 | g.node_features = torch.ones(g.node_features.shape[0], 1) 143 | if args.dataset in ['TRIANGLE', 'TRIANGLE_EX', 'MDS', 'MDS_EX']: 144 | num_classes = 2 145 | elif args.dataset in ['LCC', 'LCC_EX']: 146 | num_classes = 3 147 | else: 148 | assert(False) 149 | if args.dataset in ['MDS', 'MDS_EX']: 150 | get_labels = lambda batch_graph, model: torch.LongTensor(MDS_LOCAL(model, batch_graph)) 151 | criterion = nn.CrossEntropyLoss() 152 | else: 153 | get_labels = lambda batch_graph, model: torch.LongTensor(sum([graph.node_tags for graph in batch_graph], [])) 154 | bc = [0 for i in range(num_classes)] 155 | for G in train_graphs: 156 | for t in G.node_tags: 157 | bc[t] += 1 158 | w = torch.FloatTensor([max(bc) / bc[i] for i in range(num_classes)]).to(device) 159 | criterion = nn.CrossEntropyLoss(weight=w) 160 | else: 161 | node_classification = False 162 | graphs, num_classes = load_data(f'dataset/{args.dataset}/{args.dataset}.txt', args.degree_as_tag) 163 | 164 | ##10-fold cross validation. Conduct an experiment on the fold specified by args.fold_idx. 165 | train_graphs, test_graphs = separate_data(graphs, args.seed, args.fold_idx) 166 | 167 | criterion = nn.CrossEntropyLoss() 168 | get_labels = lambda batch_graph, model: torch.LongTensor([graph.label for graph in batch_graph]) 169 | 170 | model = GraphCNN(args.num_layers, args.num_mlp_layers, train_graphs[0].node_features.shape[1], args.hidden_dim, num_classes, args.final_dropout, args.learn_eps, args.graph_pooling_type, args.neighbor_pooling_type, args.random, node_classification, device).to(device) 171 | 172 | if args.opt == 'adam': 173 | optimizer = optim.Adam(model.parameters(), lr=args.lr) 174 | scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5) 175 | elif args.opt == 'sgd': 176 | optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9) 177 | scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.epochs, gamma=0.5) 178 | 179 | for epoch in range(1, args.epochs + 1): 180 | scheduler.step() 181 | 182 | avg_loss = train(args, model, device, train_graphs, optimizer, criterion, get_labels, epoch) 183 | acc_train, acc_test = test(args, model, device, train_graphs, test_graphs, num_classes, get_labels, epoch) 184 | 185 | if not args.filename == "": 186 | with open(args.filename, 'w') as f: 187 | f.write("%f %f %f" % (avg_loss, acc_train, acc_test)) 188 | f.write("\n") 189 | print("") 190 | 191 | print(model.eps) 192 | 193 | 194 | if __name__ == '__main__': 195 | main() 196 | -------------------------------------------------------------------------------- /models/graphcnn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | import sys 6 | sys.path.append("models/") 7 | from mlp import MLP 8 | 9 | class GraphCNN(nn.Module): 10 | def __init__(self, num_layers, num_mlp_layers, input_dim, hidden_dim, output_dim, final_dropout, learn_eps, graph_pooling_type, neighbor_pooling_type, random, node_classification, device): 11 | ''' 12 | num_layers: number of layers in the neural networks (INCLUDING the input layer) 13 | num_mlp_layers: number of layers in mlps (EXCLUDING the input layer) 14 | input_dim: dimensionality of input features 15 | hidden_dim: dimensionality of hidden units at ALL layers 16 | output_dim: number of classes for prediction 17 | final_dropout: dropout ratio on the final linear layer 18 | learn_eps: If True, learn epsilon to distinguish center nodes from neighboring nodes. If False, aggregate neighbors and center nodes altogether. 19 | neighbor_pooling_type: how to aggregate neighbors (mean, average, or max) 20 | graph_pooling_type: how to aggregate entire nodes in a graph (mean, average) 21 | device: which device to use 22 | ''' 23 | 24 | super(GraphCNN, self).__init__() 25 | 26 | if random: 27 | input_dim += 1 28 | 29 | self.final_dropout = final_dropout 30 | self.device = device 31 | self.num_layers = num_layers 32 | self.graph_pooling_type = graph_pooling_type 33 | self.neighbor_pooling_type = neighbor_pooling_type 34 | self.learn_eps = learn_eps 35 | self.eps = nn.Parameter(torch.zeros(self.num_layers-1)) 36 | self.random = random 37 | self.node_classification = node_classification 38 | 39 | ###List of MLPs 40 | self.mlps = torch.nn.ModuleList() 41 | 42 | ###List of batchnorms applied to the output of MLP (input of the final prediction linear layer) 43 | self.batch_norms = torch.nn.ModuleList() 44 | 45 | for layer in range(self.num_layers-1): 46 | if layer == 0: 47 | self.mlps.append(MLP(num_mlp_layers, input_dim, hidden_dim, hidden_dim)) 48 | else: 49 | self.mlps.append(MLP(num_mlp_layers, hidden_dim, hidden_dim, hidden_dim)) 50 | 51 | self.batch_norms.append(nn.BatchNorm1d(hidden_dim)) 52 | 53 | #Linear function that maps the hidden representation at dofferemt layers into a prediction score 54 | self.linears_prediction = torch.nn.ModuleList() 55 | for layer in range(num_layers): 56 | if layer == 0: 57 | self.linears_prediction.append(nn.Linear(input_dim, output_dim)) 58 | else: 59 | self.linears_prediction.append(nn.Linear(hidden_dim, output_dim)) 60 | 61 | #! additional linear layer 62 | self.fc1 = nn.Linear(hidden_dim, output_dim) 63 | 64 | def __preprocess_neighbors_maxpool(self, batch_graph): 65 | ###create padded_neighbor_list in concatenated graph 66 | 67 | #compute the maximum number of neighbors within the graphs in the current minibatch 68 | max_deg = max([graph.max_neighbor for graph in batch_graph]) 69 | 70 | padded_neighbor_list = [] 71 | start_idx = [0] 72 | 73 | 74 | for i, graph in enumerate(batch_graph): 75 | start_idx.append(start_idx[i] + len(graph.g)) 76 | padded_neighbors = [] 77 | for j in range(len(graph.neighbors)): 78 | #add off-set values to the neighbor indices 79 | pad = [n + start_idx[i] for n in graph.neighbors[j]] 80 | #padding, dummy data is assumed to be stored in -1 81 | pad.extend([-1]*(max_deg - len(pad))) 82 | 83 | #Add center nodes in the maxpooling if learn_eps is False, i.e., aggregate center nodes and neighbor nodes altogether. 84 | if not self.learn_eps: 85 | pad.append(j + start_idx[i]) 86 | 87 | padded_neighbors.append(pad) 88 | padded_neighbor_list.extend(padded_neighbors) 89 | 90 | return torch.LongTensor(padded_neighbor_list) 91 | 92 | 93 | def __preprocess_neighbors_sumavepool(self, batch_graph): 94 | ###create block diagonal sparse matrix 95 | 96 | edge_mat_list = [] 97 | start_idx = [0] 98 | for i, graph in enumerate(batch_graph): 99 | start_idx.append(start_idx[i] + len(graph.g)) 100 | edge_mat_list.append(graph.edge_mat + start_idx[i]) 101 | Adj_block_idx = torch.cat(edge_mat_list, 1) 102 | Adj_block_elem = torch.ones(Adj_block_idx.shape[1]) 103 | 104 | #Add self-loops in the adjacency matrix if learn_eps is False, i.e., aggregate center nodes and neighbor nodes altogether. 105 | 106 | if not self.learn_eps: 107 | num_node = start_idx[-1] 108 | self_loop_edge = torch.LongTensor([range(num_node), range(num_node)]) 109 | elem = torch.ones(num_node) 110 | Adj_block_idx = torch.cat([Adj_block_idx, self_loop_edge], 1) 111 | Adj_block_elem = torch.cat([Adj_block_elem, elem], 0) 112 | 113 | Adj_block = torch.sparse.FloatTensor(Adj_block_idx, Adj_block_elem, torch.Size([start_idx[-1],start_idx[-1]])) 114 | 115 | return Adj_block.to(self.device) 116 | 117 | 118 | def __preprocess_graphpool(self, batch_graph): 119 | ###create sum or average pooling sparse matrix over entire nodes in each graph (num graphs x num nodes) 120 | 121 | start_idx = [0] 122 | 123 | #compute the padded neighbor list 124 | for i, graph in enumerate(batch_graph): 125 | start_idx.append(start_idx[i] + len(graph.g)) 126 | 127 | idx = [] 128 | elem = [] 129 | for i, graph in enumerate(batch_graph): 130 | ###average pooling 131 | if self.graph_pooling_type == "average": 132 | elem.extend([1./len(graph.g)]*len(graph.g)) 133 | 134 | else: 135 | ###sum pooling 136 | elem.extend([1]*len(graph.g)) 137 | 138 | idx.extend([[i, j] for j in range(start_idx[i], start_idx[i+1], 1)]) 139 | elem = torch.FloatTensor(elem) 140 | idx = torch.LongTensor(idx).transpose(0,1) 141 | graph_pool = torch.sparse.FloatTensor(idx, elem, torch.Size([len(batch_graph), start_idx[-1]])) 142 | 143 | return graph_pool.to(self.device) 144 | 145 | def maxpool(self, h, padded_neighbor_list): 146 | ###Element-wise minimum will never affect max-pooling 147 | 148 | dummy = torch.min(h, dim = 0)[0] 149 | h_with_dummy = torch.cat([h, dummy.reshape((1, -1)).to(self.device)]) 150 | pooled_rep = torch.max(h_with_dummy[padded_neighbor_list], dim = 1)[0] 151 | return pooled_rep 152 | 153 | 154 | def next_layer_eps(self, h, layer, padded_neighbor_list = None, Adj_block = None): 155 | ###pooling neighboring nodes and center nodes separately by epsilon reweighting. 156 | 157 | if self.neighbor_pooling_type == "max": 158 | ##If max pooling 159 | pooled = self.maxpool(h, padded_neighbor_list) 160 | else: 161 | #If sum or average pooling 162 | pooled = torch.spmm(Adj_block, h) 163 | if self.neighbor_pooling_type == "average": 164 | #If average pooling 165 | degree = torch.spmm(Adj_block, torch.ones((Adj_block.shape[0], 1)).to(self.device)) 166 | pooled = pooled/degree 167 | 168 | #Reweights the center node representation when aggregating it with its neighbors 169 | pooled = pooled + (1 + self.eps[layer])*h 170 | pooled_rep = self.mlps[layer](pooled) 171 | h = self.batch_norms[layer](pooled_rep) 172 | 173 | #non-linearity 174 | h = F.relu(h) 175 | return h 176 | 177 | 178 | def next_layer(self, h, layer, padded_neighbor_list = None, Adj_block = None): 179 | ###pooling neighboring nodes and center nodes altogether 180 | 181 | if self.neighbor_pooling_type == "max": 182 | ##If max pooling 183 | pooled = self.maxpool(h, padded_neighbor_list) 184 | else: 185 | #If sum or average pooling 186 | pooled = torch.spmm(Adj_block, h) 187 | if self.neighbor_pooling_type == "average": 188 | #If average pooling 189 | degree = torch.spmm(Adj_block, torch.ones((Adj_block.shape[0], 1)).to(self.device)) 190 | pooled = pooled/degree 191 | 192 | #representation of neighboring and center nodes 193 | pooled_rep = self.mlps[layer](pooled) 194 | 195 | h = self.batch_norms[layer](pooled_rep) 196 | 197 | #non-linearity 198 | h = F.relu(h) 199 | return h 200 | 201 | 202 | def forward(self, batch_graph): 203 | 204 | if self.random: 205 | X_concat = torch.cat([graph.node_features for graph in batch_graph], 0) 206 | self.r = torch.randint(self.random, size=(len(X_concat), 1)).float() / self.random 207 | X_concat = torch.cat([X_concat, self.r], 1).to(self.device) 208 | else: 209 | X_concat = torch.cat([graph.node_features for graph in batch_graph], 0).to(self.device) 210 | self.r = torch.randint(100, size=(len(X_concat), 1)).float() / 100 211 | graph_pool = self.__preprocess_graphpool(batch_graph) 212 | 213 | if self.neighbor_pooling_type == "max": 214 | padded_neighbor_list = self.__preprocess_neighbors_maxpool(batch_graph) 215 | else: 216 | Adj_block = self.__preprocess_neighbors_sumavepool(batch_graph) 217 | 218 | #list of hidden representation at each layer (including input) 219 | hidden_rep = [X_concat] 220 | h = X_concat 221 | 222 | for layer in range(self.num_layers-1): 223 | if self.neighbor_pooling_type == "max" and self.learn_eps: 224 | h = self.next_layer_eps(h, layer, padded_neighbor_list = padded_neighbor_list) 225 | elif not self.neighbor_pooling_type == "max" and self.learn_eps: 226 | h = self.next_layer_eps(h, layer, Adj_block = Adj_block) 227 | elif self.neighbor_pooling_type == "max" and not self.learn_eps: 228 | h = self.next_layer(h, layer, padded_neighbor_list = padded_neighbor_list) 229 | elif not self.neighbor_pooling_type == "max" and not self.learn_eps: 230 | h = self.next_layer(h, layer, Adj_block = Adj_block) 231 | 232 | hidden_rep.append(h) 233 | 234 | if self.node_classification: 235 | self.h = h 236 | return torch.softmax(self.fc1(h), 1) 237 | 238 | score_over_layer = 0 239 | 240 | #perform pooling over all nodes in each graph in every layer 241 | for layer, h in enumerate(hidden_rep): 242 | pooled_h = torch.spmm(graph_pool, h) 243 | score_over_layer += F.dropout(self.linears_prediction[layer](pooled_h), self.final_dropout, training = self.training) 244 | 245 | return score_over_layer 246 | -------------------------------------------------------------------------------- /models/mlp.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | ###MLP with lienar output 6 | class MLP(nn.Module): 7 | def __init__(self, num_layers, input_dim, hidden_dim, output_dim): 8 | ''' 9 | num_layers: number of layers in the neural networks (EXCLUDING the input layer). If num_layers=1, this reduces to linear model. 10 | input_dim: dimensionality of input features 11 | hidden_dim: dimensionality of hidden units at ALL layers 12 | output_dim: number of classes for prediction 13 | device: which device to use 14 | ''' 15 | 16 | super(MLP, self).__init__() 17 | 18 | self.linear_or_not = True #default is linear model 19 | self.num_layers = num_layers 20 | 21 | if num_layers < 1: 22 | raise ValueError("number of layers should be positive!") 23 | elif num_layers == 1: 24 | #Linear model 25 | self.linear = nn.Linear(input_dim, output_dim) 26 | else: 27 | #Multi-layer model 28 | self.linear_or_not = False 29 | self.linears = torch.nn.ModuleList() 30 | self.batch_norms = torch.nn.ModuleList() 31 | 32 | self.linears.append(nn.Linear(input_dim, hidden_dim)) 33 | for layer in range(num_layers - 2): 34 | self.linears.append(nn.Linear(hidden_dim, hidden_dim)) 35 | self.linears.append(nn.Linear(hidden_dim, output_dim)) 36 | 37 | for layer in range(num_layers - 1): 38 | self.batch_norms.append(nn.BatchNorm1d((hidden_dim))) 39 | 40 | def forward(self, x): 41 | if self.linear_or_not: 42 | #If linear model 43 | return self.linear(x) 44 | else: 45 | #If MLP 46 | h = x 47 | for layer in range(self.num_layers - 1): 48 | h = F.relu(self.batch_norms[layer](self.linears[layer](h))) 49 | return self.linears[self.num_layers - 1](h) -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tqdm 2 | numpy 3 | networkx -------------------------------------------------------------------------------- /util.py: -------------------------------------------------------------------------------- 1 | import networkx as nx 2 | import numpy as np 3 | import random 4 | import torch 5 | from sklearn.model_selection import StratifiedKFold 6 | 7 | class S2VGraph(object): 8 | def __init__(self, g, label, node_tags=None, node_features=None): 9 | ''' 10 | g: a networkx graph 11 | label: an integer graph label 12 | node_tags: a list of integer node tags 13 | node_features: a torch float tensor, one-hot representation of the tag that is used as input to neural nets 14 | edge_mat: a torch long tensor, contain edge list, will be used to create torch sparse tensor 15 | neighbors: list of neighbors (without self-loop) 16 | ''' 17 | self.label = label 18 | self.g = g 19 | self.node_tags = node_tags 20 | self.neighbors = [] 21 | self.node_features = 0 22 | self.edge_mat = 0 23 | 24 | self.max_neighbor = 0 25 | 26 | 27 | def load_data(dataset, degree_as_tag): 28 | ''' 29 | dataset: name of dataset 30 | test_proportion: ratio of test train split 31 | seed: random seed for random splitting of dataset 32 | ''' 33 | 34 | print('loading data') 35 | g_list = [] 36 | label_dict = {} 37 | feat_dict = {} 38 | 39 | with open(dataset, 'r') as f: 40 | n_g = int(f.readline().strip()) 41 | for i in range(n_g): 42 | row = f.readline().strip().split() 43 | n, l = [int(w) for w in row] 44 | if not l in label_dict: 45 | mapped = len(label_dict) 46 | label_dict[l] = mapped 47 | g = nx.Graph() 48 | node_tags = [] 49 | node_features = [] 50 | n_edges = 0 51 | for j in range(n): 52 | g.add_node(j) 53 | row = f.readline().strip().split() 54 | tmp = int(row[1]) + 2 55 | if tmp == len(row): 56 | # no node attributes 57 | row = [int(w) for w in row] 58 | attr = None 59 | else: 60 | row, attr = [int(w) for w in row[:tmp]], np.array([float(w) for w in row[tmp:]]) 61 | if not row[0] in feat_dict: 62 | mapped = len(feat_dict) 63 | feat_dict[row[0]] = mapped 64 | node_tags.append(feat_dict[row[0]]) 65 | 66 | if tmp > len(row): 67 | node_features.append(attr) 68 | 69 | n_edges += row[1] 70 | for k in range(2, len(row)): 71 | g.add_edge(j, row[k]) 72 | 73 | if node_features != []: 74 | node_features = np.stack(node_features) 75 | node_feature_flag = True 76 | else: 77 | node_features = None 78 | node_feature_flag = False 79 | 80 | assert len(g) == n 81 | 82 | g_list.append(S2VGraph(g, l, node_tags)) 83 | 84 | #add labels and edge_mat 85 | for g in g_list: 86 | g.neighbors = [[] for i in range(len(g.g))] 87 | for i, j in g.g.edges(): 88 | g.neighbors[i].append(j) 89 | g.neighbors[j].append(i) 90 | degree_list = [] 91 | for i in range(len(g.g)): 92 | g.neighbors[i] = g.neighbors[i] 93 | degree_list.append(len(g.neighbors[i])) 94 | g.max_neighbor = max(degree_list) 95 | 96 | g.label = label_dict[g.label] 97 | 98 | edges = [list(pair) for pair in g.g.edges()] 99 | edges.extend([[i, j] for j, i in edges]) 100 | 101 | deg_list = list(dict(g.g.degree(range(len(g.g)))).values()) 102 | g.edge_mat = torch.LongTensor(edges).transpose(0,1) 103 | 104 | if degree_as_tag: 105 | for g in g_list: 106 | g.node_tags = list(dict(g.g.degree).values()) 107 | 108 | #Extracting unique tag labels 109 | tagset = set([]) 110 | for g in g_list: 111 | tagset = tagset.union(set(g.node_tags)) 112 | 113 | tagset = list(tagset) 114 | tag2index = {tagset[i]:i for i in range(len(tagset))} 115 | 116 | for g in g_list: 117 | g.node_features = torch.zeros(len(g.node_tags), len(tagset)) 118 | g.node_features[range(len(g.node_tags)), [tag2index[tag] for tag in g.node_tags]] = 1 119 | 120 | 121 | print('# classes: %d' % len(label_dict)) 122 | print('# maximum node tag: %d' % len(tagset)) 123 | 124 | print("# data: %d" % len(g_list)) 125 | 126 | return g_list, len(label_dict) 127 | 128 | def separate_data(graph_list, seed, fold_idx): 129 | assert 0 <= fold_idx and fold_idx < 10, "fold_idx must be from 0 to 9." 130 | skf = StratifiedKFold(n_splits=10, shuffle = True, random_state = seed) 131 | 132 | labels = [graph.label for graph in graph_list] 133 | idx_list = [] 134 | for idx in skf.split(np.zeros(len(labels)), labels): 135 | idx_list.append(idx) 136 | train_idx, test_idx = idx_list[fold_idx] 137 | 138 | train_graph_list = [graph_list[i] for i in train_idx] 139 | test_graph_list = [graph_list[i] for i in test_idx] 140 | 141 | return train_graph_list, test_graph_list 142 | 143 | 144 | --------------------------------------------------------------------------------