├── .gitignore ├── LICENCE ├── README.md ├── figure.png ├── gae ├── __init__.py ├── data │ ├── ind.citeseer.allx │ ├── ind.citeseer.graph │ ├── ind.citeseer.test.index │ ├── ind.citeseer.tx │ ├── ind.citeseer.x │ ├── ind.cora.allx │ ├── ind.cora.graph │ ├── ind.cora.test.index │ ├── ind.cora.tx │ ├── ind.cora.x │ ├── ind.pubmed.allx │ ├── ind.pubmed.graph │ ├── ind.pubmed.test.index │ ├── ind.pubmed.tx │ └── ind.pubmed.x ├── initializations.py ├── input_data.py ├── layers.py ├── model.py ├── optimizer.py ├── preprocessing.py └── train.py └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | *.pyc 3 | build/ 4 | dist/ 5 | *.egg-info/ -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | The MIT License 2 | 3 | Copyright (c) 2017 Thomas Kipf 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in 13 | all copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 21 | THE SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Graph Auto-Encoders 2 | ============ 3 | 4 | This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper: 5 | 6 | T. N. Kipf, M. Welling, [Variational Graph Auto-Encoders](https://arxiv.org/abs/1611.07308), NIPS Workshop on Bayesian Deep Learning (2016) 7 | 8 | Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. 9 | 10 | ![(Variational) Graph Auto-Encoder](figure.png) 11 | 12 | GAEs have successfully been used for: 13 | * Link prediction in large-scale relational data: M. Schlichtkrull & T. N. Kipf et al., [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103) (2017), 14 | * Matrix completion / recommendation with side information: R. Berg et al., [Graph Convolutional Matrix Completion](https://arxiv.org/abs/1706.02263) (2017). 15 | 16 | 17 | GAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs: 18 | 19 | T. N. Kipf, M. Welling, [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), ICLR (2017). 20 | 21 | A high-level introduction is given in our blog post: 22 | 23 | Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016) 24 | 25 | 26 | 27 | ## Installation 28 | 29 | ```bash 30 | python setup.py install 31 | ``` 32 | 33 | ## Requirements 34 | * TensorFlow (1.0 or later) 35 | * python 2.7 36 | * networkx 37 | * scikit-learn 38 | * scipy 39 | 40 | ## Run the demo 41 | 42 | ```bash 43 | python train.py 44 | ``` 45 | 46 | ## Data 47 | 48 | In order to use your own data, you have to provide 49 | * an N by N adjacency matrix (N is the number of nodes), and 50 | * an N by D feature matrix (D is the number of features per node) -- optional 51 | 52 | Have a look at the `load_data()` function in `input_data.py` for an example. 53 | 54 | In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/ and here (in a different format): https://github.com/kimiyoung/planetoid 55 | 56 | You can specify a dataset as follows: 57 | 58 | ```bash 59 | python train.py --dataset citeseer 60 | ``` 61 | 62 | (or by editing `train.py`) 63 | 64 | ## Models 65 | 66 | You can choose between the following models: 67 | * `gcn_ae`: Graph Auto-Encoder (with GCN encoder) 68 | * `gcn_vae`: Variational Graph Auto-Encoder (with GCN encoder) 69 | 70 | ## Cite 71 | 72 | Please cite our paper if you use this code in your own work: 73 | 74 | ``` 75 | @article{kipf2016variational, 76 | title={Variational Graph Auto-Encoders}, 77 | author={Kipf, Thomas N and Welling, Max}, 78 | journal={NIPS Workshop on Bayesian Deep Learning}, 79 | year={2016} 80 | } 81 | ``` 82 | -------------------------------------------------------------------------------- /figure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gae/0ebbe9b9a8f496eb12deb9aa6a62e7016b5a5ac3/figure.png -------------------------------------------------------------------------------- /gae/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | from __future__ import division 3 | -------------------------------------------------------------------------------- /gae/data/ind.citeseer.allx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gae/0ebbe9b9a8f496eb12deb9aa6a62e7016b5a5ac3/gae/data/ind.citeseer.allx -------------------------------------------------------------------------------- /gae/data/ind.citeseer.graph: -------------------------------------------------------------------------------- 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19622 849 | 19184 850 | 18977 851 | 19702 852 | 19539 853 | 19329 854 | 19095 855 | 19675 856 | 18972 857 | 19514 858 | 19703 859 | 19188 860 | 18866 861 | 18812 862 | 19314 863 | 18822 864 | 18845 865 | 19494 866 | 19411 867 | 18916 868 | 19686 869 | 18967 870 | 19294 871 | 19143 872 | 19204 873 | 18805 874 | 19689 875 | 19233 876 | 18758 877 | 18748 878 | 19011 879 | 19685 880 | 19336 881 | 19608 882 | 19454 883 | 19124 884 | 18868 885 | 18807 886 | 19544 887 | 19621 888 | 19228 889 | 19154 890 | 19141 891 | 19145 892 | 19153 893 | 18860 894 | 19163 895 | 19393 896 | 19268 897 | 19160 898 | 19305 899 | 19259 900 | 19471 901 | 19524 902 | 18783 903 | 19396 904 | 18894 905 | 19430 906 | 19690 907 | 19348 908 | 19597 909 | 19592 910 | 19677 911 | 18889 912 | 19331 913 | 18773 914 | 19137 915 | 19009 916 | 18932 917 | 19599 918 | 18816 919 | 19054 920 | 19067 921 | 19477 922 | 19191 923 | 18921 924 | 18940 925 | 19578 926 | 19183 927 | 19004 928 | 19072 929 | 19710 930 | 19005 931 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gae/0ebbe9b9a8f496eb12deb9aa6a62e7016b5a5ac3/gae/data/ind.pubmed.tx -------------------------------------------------------------------------------- /gae/data/ind.pubmed.x: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tkipf/gae/0ebbe9b9a8f496eb12deb9aa6a62e7016b5a5ac3/gae/data/ind.pubmed.x -------------------------------------------------------------------------------- /gae/initializations.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | 4 | def weight_variable_glorot(input_dim, output_dim, name=""): 5 | """Create a weight variable with Glorot & Bengio (AISTATS 2010) 6 | initialization. 7 | """ 8 | init_range = np.sqrt(6.0 / (input_dim + output_dim)) 9 | initial = tf.random_uniform([input_dim, output_dim], minval=-init_range, 10 | maxval=init_range, dtype=tf.float32) 11 | return tf.Variable(initial, name=name) 12 | -------------------------------------------------------------------------------- /gae/input_data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sys 3 | import pickle as pkl 4 | import networkx as nx 5 | import scipy.sparse as sp 6 | 7 | 8 | def parse_index_file(filename): 9 | index = [] 10 | for line in open(filename): 11 | index.append(int(line.strip())) 12 | return index 13 | 14 | 15 | def load_data(dataset): 16 | # load the data: x, tx, allx, graph 17 | names = ['x', 'tx', 'allx', 'graph'] 18 | objects = [] 19 | for i in range(len(names)): 20 | with open("data/ind.{}.{}".format(dataset, names[i]), 'rb') as f: 21 | if sys.version_info > (3, 0): 22 | objects.append(pkl.load(f, encoding='latin1')) 23 | else: 24 | objects.append(pkl.load(f)) 25 | x, tx, allx, graph = tuple(objects) 26 | test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset)) 27 | test_idx_range = np.sort(test_idx_reorder) 28 | 29 | if dataset == 'citeseer': 30 | # Fix citeseer dataset (there are some isolated nodes in the graph) 31 | # Find isolated nodes, add them as zero-vecs into the right position 32 | test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) 33 | tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) 34 | tx_extended[test_idx_range-min(test_idx_range), :] = tx 35 | tx = tx_extended 36 | 37 | features = sp.vstack((allx, tx)).tolil() 38 | features[test_idx_reorder, :] = features[test_idx_range, :] 39 | adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) 40 | 41 | return adj, features 42 | -------------------------------------------------------------------------------- /gae/layers.py: -------------------------------------------------------------------------------- 1 | from gae.initializations import * 2 | import tensorflow as tf 3 | 4 | flags = tf.app.flags 5 | FLAGS = flags.FLAGS 6 | 7 | # global unique layer ID dictionary for layer name assignment 8 | _LAYER_UIDS = {} 9 | 10 | 11 | def get_layer_uid(layer_name=''): 12 | """Helper function, assigns unique layer IDs 13 | """ 14 | if layer_name not in _LAYER_UIDS: 15 | _LAYER_UIDS[layer_name] = 1 16 | return 1 17 | else: 18 | _LAYER_UIDS[layer_name] += 1 19 | return _LAYER_UIDS[layer_name] 20 | 21 | 22 | def dropout_sparse(x, keep_prob, num_nonzero_elems): 23 | """Dropout for sparse tensors. Currently fails for very large sparse tensors (>1M elements) 24 | """ 25 | noise_shape = [num_nonzero_elems] 26 | random_tensor = keep_prob 27 | random_tensor += tf.random_uniform(noise_shape) 28 | dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool) 29 | pre_out = tf.sparse_retain(x, dropout_mask) 30 | return pre_out * (1./keep_prob) 31 | 32 | 33 | class Layer(object): 34 | """Base layer class. Defines basic API for all layer objects. 35 | 36 | # Properties 37 | name: String, defines the variable scope of the layer. 38 | 39 | # Methods 40 | _call(inputs): Defines computation graph of layer 41 | (i.e. takes input, returns output) 42 | __call__(inputs): Wrapper for _call() 43 | """ 44 | def __init__(self, **kwargs): 45 | allowed_kwargs = {'name', 'logging'} 46 | for kwarg in kwargs.keys(): 47 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 48 | name = kwargs.get('name') 49 | if not name: 50 | layer = self.__class__.__name__.lower() 51 | name = layer + '_' + str(get_layer_uid(layer)) 52 | self.name = name 53 | self.vars = {} 54 | logging = kwargs.get('logging', False) 55 | self.logging = logging 56 | self.issparse = False 57 | 58 | def _call(self, inputs): 59 | return inputs 60 | 61 | def __call__(self, inputs): 62 | with tf.name_scope(self.name): 63 | outputs = self._call(inputs) 64 | return outputs 65 | 66 | 67 | class GraphConvolution(Layer): 68 | """Basic graph convolution layer for undirected graph without edge labels.""" 69 | def __init__(self, input_dim, output_dim, adj, dropout=0., act=tf.nn.relu, **kwargs): 70 | super(GraphConvolution, self).__init__(**kwargs) 71 | with tf.variable_scope(self.name + '_vars'): 72 | self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights") 73 | self.dropout = dropout 74 | self.adj = adj 75 | self.act = act 76 | 77 | def _call(self, inputs): 78 | x = inputs 79 | x = tf.nn.dropout(x, 1-self.dropout) 80 | x = tf.matmul(x, self.vars['weights']) 81 | x = tf.sparse_tensor_dense_matmul(self.adj, x) 82 | outputs = self.act(x) 83 | return outputs 84 | 85 | 86 | class GraphConvolutionSparse(Layer): 87 | """Graph convolution layer for sparse inputs.""" 88 | def __init__(self, input_dim, output_dim, adj, features_nonzero, dropout=0., act=tf.nn.relu, **kwargs): 89 | super(GraphConvolutionSparse, self).__init__(**kwargs) 90 | with tf.variable_scope(self.name + '_vars'): 91 | self.vars['weights'] = weight_variable_glorot(input_dim, output_dim, name="weights") 92 | self.dropout = dropout 93 | self.adj = adj 94 | self.act = act 95 | self.issparse = True 96 | self.features_nonzero = features_nonzero 97 | 98 | def _call(self, inputs): 99 | x = inputs 100 | x = dropout_sparse(x, 1-self.dropout, self.features_nonzero) 101 | x = tf.sparse_tensor_dense_matmul(x, self.vars['weights']) 102 | x = tf.sparse_tensor_dense_matmul(self.adj, x) 103 | outputs = self.act(x) 104 | return outputs 105 | 106 | 107 | class InnerProductDecoder(Layer): 108 | """Decoder model layer for link prediction.""" 109 | def __init__(self, input_dim, dropout=0., act=tf.nn.sigmoid, **kwargs): 110 | super(InnerProductDecoder, self).__init__(**kwargs) 111 | self.dropout = dropout 112 | self.act = act 113 | 114 | def _call(self, inputs): 115 | inputs = tf.nn.dropout(inputs, 1-self.dropout) 116 | x = tf.transpose(inputs) 117 | x = tf.matmul(inputs, x) 118 | x = tf.reshape(x, [-1]) 119 | outputs = self.act(x) 120 | return outputs 121 | -------------------------------------------------------------------------------- /gae/model.py: -------------------------------------------------------------------------------- 1 | from gae.layers import GraphConvolution, GraphConvolutionSparse, InnerProductDecoder 2 | import tensorflow as tf 3 | 4 | flags = tf.app.flags 5 | FLAGS = flags.FLAGS 6 | 7 | 8 | class Model(object): 9 | def __init__(self, **kwargs): 10 | allowed_kwargs = {'name', 'logging'} 11 | for kwarg in kwargs.keys(): 12 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 13 | 14 | for kwarg in kwargs.keys(): 15 | assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg 16 | name = kwargs.get('name') 17 | if not name: 18 | name = self.__class__.__name__.lower() 19 | self.name = name 20 | 21 | logging = kwargs.get('logging', False) 22 | self.logging = logging 23 | 24 | self.vars = {} 25 | 26 | def _build(self): 27 | raise NotImplementedError 28 | 29 | def build(self): 30 | """ Wrapper for _build() """ 31 | with tf.variable_scope(self.name): 32 | self._build() 33 | variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name) 34 | self.vars = {var.name: var for var in variables} 35 | 36 | def fit(self): 37 | pass 38 | 39 | def predict(self): 40 | pass 41 | 42 | 43 | class GCNModelAE(Model): 44 | def __init__(self, placeholders, num_features, features_nonzero, **kwargs): 45 | super(GCNModelAE, self).__init__(**kwargs) 46 | 47 | self.inputs = placeholders['features'] 48 | self.input_dim = num_features 49 | self.features_nonzero = features_nonzero 50 | self.adj = placeholders['adj'] 51 | self.dropout = placeholders['dropout'] 52 | self.build() 53 | 54 | def _build(self): 55 | self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, 56 | output_dim=FLAGS.hidden1, 57 | adj=self.adj, 58 | features_nonzero=self.features_nonzero, 59 | act=tf.nn.relu, 60 | dropout=self.dropout, 61 | logging=self.logging)(self.inputs) 62 | 63 | self.embeddings = GraphConvolution(input_dim=FLAGS.hidden1, 64 | output_dim=FLAGS.hidden2, 65 | adj=self.adj, 66 | act=lambda x: x, 67 | dropout=self.dropout, 68 | logging=self.logging)(self.hidden1) 69 | 70 | self.z_mean = self.embeddings 71 | 72 | self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2, 73 | act=lambda x: x, 74 | logging=self.logging)(self.embeddings) 75 | 76 | 77 | class GCNModelVAE(Model): 78 | def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs): 79 | super(GCNModelVAE, self).__init__(**kwargs) 80 | 81 | self.inputs = placeholders['features'] 82 | self.input_dim = num_features 83 | self.features_nonzero = features_nonzero 84 | self.n_samples = num_nodes 85 | self.adj = placeholders['adj'] 86 | self.dropout = placeholders['dropout'] 87 | self.build() 88 | 89 | def _build(self): 90 | self.hidden1 = GraphConvolutionSparse(input_dim=self.input_dim, 91 | output_dim=FLAGS.hidden1, 92 | adj=self.adj, 93 | features_nonzero=self.features_nonzero, 94 | act=tf.nn.relu, 95 | dropout=self.dropout, 96 | logging=self.logging)(self.inputs) 97 | 98 | self.z_mean = GraphConvolution(input_dim=FLAGS.hidden1, 99 | output_dim=FLAGS.hidden2, 100 | adj=self.adj, 101 | act=lambda x: x, 102 | dropout=self.dropout, 103 | logging=self.logging)(self.hidden1) 104 | 105 | self.z_log_std = GraphConvolution(input_dim=FLAGS.hidden1, 106 | output_dim=FLAGS.hidden2, 107 | adj=self.adj, 108 | act=lambda x: x, 109 | dropout=self.dropout, 110 | logging=self.logging)(self.hidden1) 111 | 112 | self.z = self.z_mean + tf.random_normal([self.n_samples, FLAGS.hidden2]) * tf.exp(self.z_log_std) 113 | 114 | self.reconstructions = InnerProductDecoder(input_dim=FLAGS.hidden2, 115 | act=lambda x: x, 116 | logging=self.logging)(self.z) 117 | -------------------------------------------------------------------------------- /gae/optimizer.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | flags = tf.app.flags 4 | FLAGS = flags.FLAGS 5 | 6 | 7 | class OptimizerAE(object): 8 | def __init__(self, preds, labels, pos_weight, norm): 9 | preds_sub = preds 10 | labels_sub = labels 11 | 12 | self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) 13 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer 14 | 15 | self.opt_op = self.optimizer.minimize(self.cost) 16 | self.grads_vars = self.optimizer.compute_gradients(self.cost) 17 | 18 | self.correct_prediction = tf.equal(tf.cast(tf.greater_equal(tf.sigmoid(preds_sub), 0.5), tf.int32), 19 | tf.cast(labels_sub, tf.int32)) 20 | self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32)) 21 | 22 | 23 | class OptimizerVAE(object): 24 | def __init__(self, preds, labels, model, num_nodes, pos_weight, norm): 25 | preds_sub = preds 26 | labels_sub = labels 27 | 28 | self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) 29 | self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer 30 | 31 | # Latent loss 32 | self.log_lik = self.cost 33 | self.kl = (0.5 / num_nodes) * tf.reduce_mean(tf.reduce_sum(1 + 2 * model.z_log_std - tf.square(model.z_mean) - 34 | tf.square(tf.exp(model.z_log_std)), 1)) 35 | self.cost -= self.kl 36 | 37 | self.opt_op = self.optimizer.minimize(self.cost) 38 | self.grads_vars = self.optimizer.compute_gradients(self.cost) 39 | 40 | self.correct_prediction = tf.equal(tf.cast(tf.greater_equal(tf.sigmoid(preds_sub), 0.5), tf.int32), 41 | tf.cast(labels_sub, tf.int32)) 42 | self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32)) 43 | -------------------------------------------------------------------------------- /gae/preprocessing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.sparse as sp 3 | 4 | 5 | def sparse_to_tuple(sparse_mx): 6 | if not sp.isspmatrix_coo(sparse_mx): 7 | sparse_mx = sparse_mx.tocoo() 8 | coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() 9 | values = sparse_mx.data 10 | shape = sparse_mx.shape 11 | return coords, values, shape 12 | 13 | 14 | def preprocess_graph(adj): 15 | adj = sp.coo_matrix(adj) 16 | adj_ = adj + sp.eye(adj.shape[0]) 17 | rowsum = np.array(adj_.sum(1)) 18 | degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten()) 19 | adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo() 20 | return sparse_to_tuple(adj_normalized) 21 | 22 | 23 | def construct_feed_dict(adj_normalized, adj, features, placeholders): 24 | # construct feed dictionary 25 | feed_dict = dict() 26 | feed_dict.update({placeholders['features']: features}) 27 | feed_dict.update({placeholders['adj']: adj_normalized}) 28 | feed_dict.update({placeholders['adj_orig']: adj}) 29 | return feed_dict 30 | 31 | 32 | def mask_test_edges(adj): 33 | # Function to build test set with 10% positive links 34 | # NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper. 35 | # TODO: Clean up. 36 | 37 | # Remove diagonal elements 38 | adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) 39 | adj.eliminate_zeros() 40 | # Check that diag is zero: 41 | assert np.diag(adj.todense()).sum() == 0 42 | 43 | adj_triu = sp.triu(adj) 44 | adj_tuple = sparse_to_tuple(adj_triu) 45 | edges = adj_tuple[0] 46 | edges_all = sparse_to_tuple(adj)[0] 47 | num_test = int(np.floor(edges.shape[0] / 10.)) 48 | num_val = int(np.floor(edges.shape[0] / 20.)) 49 | 50 | all_edge_idx = list(range(edges.shape[0])) 51 | np.random.shuffle(all_edge_idx) 52 | val_edge_idx = all_edge_idx[:num_val] 53 | test_edge_idx = all_edge_idx[num_val:(num_val + num_test)] 54 | test_edges = edges[test_edge_idx] 55 | val_edges = edges[val_edge_idx] 56 | train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0) 57 | 58 | def ismember(a, b, tol=5): 59 | rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1) 60 | return np.any(rows_close) 61 | 62 | test_edges_false = [] 63 | while len(test_edges_false) < len(test_edges): 64 | idx_i = np.random.randint(0, adj.shape[0]) 65 | idx_j = np.random.randint(0, adj.shape[0]) 66 | if idx_i == idx_j: 67 | continue 68 | if ismember([idx_i, idx_j], edges_all): 69 | continue 70 | if test_edges_false: 71 | if ismember([idx_j, idx_i], np.array(test_edges_false)): 72 | continue 73 | if ismember([idx_i, idx_j], np.array(test_edges_false)): 74 | continue 75 | test_edges_false.append([idx_i, idx_j]) 76 | 77 | val_edges_false = [] 78 | while len(val_edges_false) < len(val_edges): 79 | idx_i = np.random.randint(0, adj.shape[0]) 80 | idx_j = np.random.randint(0, adj.shape[0]) 81 | if idx_i == idx_j: 82 | continue 83 | if ismember([idx_i, idx_j], train_edges): 84 | continue 85 | if ismember([idx_j, idx_i], train_edges): 86 | continue 87 | if ismember([idx_i, idx_j], val_edges): 88 | continue 89 | if ismember([idx_j, idx_i], val_edges): 90 | continue 91 | if val_edges_false: 92 | if ismember([idx_j, idx_i], np.array(val_edges_false)): 93 | continue 94 | if ismember([idx_i, idx_j], np.array(val_edges_false)): 95 | continue 96 | val_edges_false.append([idx_i, idx_j]) 97 | 98 | assert ~ismember(test_edges_false, edges_all) 99 | assert ~ismember(val_edges_false, edges_all) 100 | assert ~ismember(val_edges, train_edges) 101 | assert ~ismember(test_edges, train_edges) 102 | assert ~ismember(val_edges, test_edges) 103 | 104 | data = np.ones(train_edges.shape[0]) 105 | 106 | # Re-build adj matrix 107 | adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape) 108 | adj_train = adj_train + adj_train.T 109 | 110 | # NOTE: these edge lists only contain single direction of edge! 111 | return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false 112 | -------------------------------------------------------------------------------- /gae/train.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | 4 | import time 5 | import os 6 | 7 | # Train on CPU (hide GPU) due to memory constraints 8 | os.environ['CUDA_VISIBLE_DEVICES'] = "" 9 | 10 | import tensorflow as tf 11 | import numpy as np 12 | import scipy.sparse as sp 13 | 14 | from sklearn.metrics import roc_auc_score 15 | from sklearn.metrics import average_precision_score 16 | 17 | from gae.optimizer import OptimizerAE, OptimizerVAE 18 | from gae.input_data import load_data 19 | from gae.model import GCNModelAE, GCNModelVAE 20 | from gae.preprocessing import preprocess_graph, construct_feed_dict, sparse_to_tuple, mask_test_edges 21 | 22 | # Settings 23 | flags = tf.app.flags 24 | FLAGS = flags.FLAGS 25 | flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.') 26 | flags.DEFINE_integer('epochs', 200, 'Number of epochs to train.') 27 | flags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.') 28 | flags.DEFINE_integer('hidden2', 16, 'Number of units in hidden layer 2.') 29 | flags.DEFINE_float('weight_decay', 0., 'Weight for L2 loss on embedding matrix.') 30 | flags.DEFINE_float('dropout', 0., 'Dropout rate (1 - keep probability).') 31 | 32 | flags.DEFINE_string('model', 'gcn_ae', 'Model string.') 33 | flags.DEFINE_string('dataset', 'cora', 'Dataset string.') 34 | flags.DEFINE_integer('features', 1, 'Whether to use features (1) or not (0).') 35 | 36 | model_str = FLAGS.model 37 | dataset_str = FLAGS.dataset 38 | 39 | # Load data 40 | adj, features = load_data(dataset_str) 41 | 42 | # Store original adjacency matrix (without diagonal entries) for later 43 | adj_orig = adj 44 | adj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape) 45 | adj_orig.eliminate_zeros() 46 | 47 | adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(adj) 48 | adj = adj_train 49 | 50 | if FLAGS.features == 0: 51 | features = sp.identity(features.shape[0]) # featureless 52 | 53 | # Some preprocessing 54 | adj_norm = preprocess_graph(adj) 55 | 56 | # Define placeholders 57 | placeholders = { 58 | 'features': tf.sparse_placeholder(tf.float32), 59 | 'adj': tf.sparse_placeholder(tf.float32), 60 | 'adj_orig': tf.sparse_placeholder(tf.float32), 61 | 'dropout': tf.placeholder_with_default(0., shape=()) 62 | } 63 | 64 | num_nodes = adj.shape[0] 65 | 66 | features = sparse_to_tuple(features.tocoo()) 67 | num_features = features[2][1] 68 | features_nonzero = features[1].shape[0] 69 | 70 | # Create model 71 | model = None 72 | if model_str == 'gcn_ae': 73 | model = GCNModelAE(placeholders, num_features, features_nonzero) 74 | elif model_str == 'gcn_vae': 75 | model = GCNModelVAE(placeholders, num_features, num_nodes, features_nonzero) 76 | 77 | pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum() 78 | norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2) 79 | 80 | # Optimizer 81 | with tf.name_scope('optimizer'): 82 | if model_str == 'gcn_ae': 83 | opt = OptimizerAE(preds=model.reconstructions, 84 | labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'], 85 | validate_indices=False), [-1]), 86 | pos_weight=pos_weight, 87 | norm=norm) 88 | elif model_str == 'gcn_vae': 89 | opt = OptimizerVAE(preds=model.reconstructions, 90 | labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'], 91 | validate_indices=False), [-1]), 92 | model=model, num_nodes=num_nodes, 93 | pos_weight=pos_weight, 94 | norm=norm) 95 | 96 | # Initialize session 97 | sess = tf.Session() 98 | sess.run(tf.global_variables_initializer()) 99 | 100 | cost_val = [] 101 | acc_val = [] 102 | 103 | 104 | def get_roc_score(edges_pos, edges_neg, emb=None): 105 | if emb is None: 106 | feed_dict.update({placeholders['dropout']: 0}) 107 | emb = sess.run(model.z_mean, feed_dict=feed_dict) 108 | 109 | def sigmoid(x): 110 | return 1 / (1 + np.exp(-x)) 111 | 112 | # Predict on test set of edges 113 | adj_rec = np.dot(emb, emb.T) 114 | preds = [] 115 | pos = [] 116 | for e in edges_pos: 117 | preds.append(sigmoid(adj_rec[e[0], e[1]])) 118 | pos.append(adj_orig[e[0], e[1]]) 119 | 120 | preds_neg = [] 121 | neg = [] 122 | for e in edges_neg: 123 | preds_neg.append(sigmoid(adj_rec[e[0], e[1]])) 124 | neg.append(adj_orig[e[0], e[1]]) 125 | 126 | preds_all = np.hstack([preds, preds_neg]) 127 | labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds_neg))]) 128 | roc_score = roc_auc_score(labels_all, preds_all) 129 | ap_score = average_precision_score(labels_all, preds_all) 130 | 131 | return roc_score, ap_score 132 | 133 | 134 | cost_val = [] 135 | acc_val = [] 136 | val_roc_score = [] 137 | 138 | adj_label = adj_train + sp.eye(adj_train.shape[0]) 139 | adj_label = sparse_to_tuple(adj_label) 140 | 141 | # Train model 142 | for epoch in range(FLAGS.epochs): 143 | 144 | t = time.time() 145 | # Construct feed dictionary 146 | feed_dict = construct_feed_dict(adj_norm, adj_label, features, placeholders) 147 | feed_dict.update({placeholders['dropout']: FLAGS.dropout}) 148 | # Run single weight update 149 | outs = sess.run([opt.opt_op, opt.cost, opt.accuracy], feed_dict=feed_dict) 150 | 151 | # Compute average loss 152 | avg_cost = outs[1] 153 | avg_accuracy = outs[2] 154 | 155 | roc_curr, ap_curr = get_roc_score(val_edges, val_edges_false) 156 | val_roc_score.append(roc_curr) 157 | 158 | print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(avg_cost), 159 | "train_acc=", "{:.5f}".format(avg_accuracy), "val_roc=", "{:.5f}".format(val_roc_score[-1]), 160 | "val_ap=", "{:.5f}".format(ap_curr), 161 | "time=", "{:.5f}".format(time.time() - t)) 162 | 163 | print("Optimization Finished!") 164 | 165 | roc_score, ap_score = get_roc_score(test_edges, test_edges_false) 166 | print('Test ROC score: ' + str(roc_score)) 167 | print('Test AP score: ' + str(ap_score)) 168 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | from setuptools import find_packages 3 | 4 | setup(name='gae', 5 | version='0.0.1', 6 | description='Implementation of (Variational) Graph Auto-Encoders in Tensorflow', 7 | author='Thomas Kipf', 8 | author_email='thomas.kipf@gmail.com', 9 | url='https://tkipf.github.io', 10 | download_url='https://github.com/tkipf/gae', 11 | license='MIT', 12 | install_requires=['numpy', 13 | 'tensorflow', 14 | 'networkx', 15 | 'scikit-learn', 16 | 'scipy', 17 | ], 18 | extras_require={ 19 | 'visualization': ['matplotlib'], 20 | }, 21 | package_data={'gae': ['README.md']}, 22 | packages=find_packages()) 23 | --------------------------------------------------------------------------------