├── .github └── FUNDING.yml ├── LICENSE ├── README.md ├── atlas.png ├── awesome.py ├── chapters ├── deep_learning.md ├── fingerprints.md ├── kernels.md └── matrix_factorization.md ├── code-of-conduct.md └── contributing.md /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | github: [benedekrozemberczki] 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | CC0 1.0 Universal 2 | 3 | Statement of Purpose 4 | 5 | The laws of most jurisdictions throughout the world automatically confer 6 | exclusive Copyright and Related Rights (defined below) upon the creator and 7 | subsequent owner(s) (each and all, an "owner") of an original work of 8 | authorship and/or a database (each, a "Work"). 9 | 10 | Certain owners wish to permanently relinquish those rights to a Work for the 11 | purpose of contributing to a commons of creative, cultural and scientific 12 | works ("Commons") that the public can reliably and without fear of later 13 | claims of infringement build upon, modify, incorporate in other works, reuse 14 | and redistribute as freely as possible in any form whatsoever and for any 15 | purposes, including without limitation commercial purposes. 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14 | 15 |

16 | 17 | ------------------------------------------------- 18 | 19 | ## Contents 20 | 21 | 1. [Matrix Factorization](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/matrix_factorization.md) 22 | 2. [Spectral and Statistical Fingerprints](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/fingerprints.md) 23 | 3. [Deep Learning](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/deep_learning.md) 24 | 4. [Graph Kernels](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/kernels.md) 25 | 26 | ----------------------------------------------- 27 | 28 | **License** 29 | 30 | - [CC0 Universal](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/LICENSE) 31 | -------------------------------------------------------------------------------- /atlas.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/benedekrozemberczki/awesome-graph-classification/39476c5707841930dfa4138aeadba55ab6383bf8/atlas.png -------------------------------------------------------------------------------- /awesome.py: -------------------------------------------------------------------------------- 1 | __author__ = "Benedek Rozemberczki" 2 | __maintainer__ = "Benedek Rozemberczki" 3 | __email__ = "benedek.rozemberczki@gmail.com" 4 | __status__ = "Production" 5 | -------------------------------------------------------------------------------- /chapters/deep_learning.md: -------------------------------------------------------------------------------- 1 | ## Deep Learning 2 | ### 2020 3 | 4 | - **Principal Neighbourhood Aggregation for Graph Nets (ArXiV 2020)** 5 | - Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković 6 | - [[Paper]](https://arxiv.org/abs/2004.05718) 7 | - [[Python Reference]](https://github.com/lukecavabarrett/pna) 8 | 9 | - **ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (AAAI 2020)** 10 | - Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar 11 | - [[Paper]](https://arxiv.org/abs/1911.07979) 12 | - [[Python Reference]](https://github.com/malllabiisc/ASAP) 13 | 14 | - **PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures (AISTATS 2020)** 15 | - Mathieu Carriere, Frederic Chazal, Yuichi Ike, Theo Lacombe, Martin Royer, Yuhei Umeda 16 | - [[Paper]](http://proceedings.mlr.press/v108/carriere20a.html) 17 | - [[Python Reference]](https://github.com/MathieuCarriere/perslay) 18 | 19 | - **Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks (ArXiv 2020)** 20 | - Muhammet Balcilar, Guillaume Renton, Pierre Heroux, Benoit Gauzere, Sebastien Adam, Paul Honeine 21 | - [[Paper]](https://arxiv.org/abs/2003.11702) 22 | - [[Python Reference]](https://github.com/balcilar/Spectral-Designed-Graph-Convolutions) 23 | 24 | - **Segmented Graph-Bert for Graph Instance Modeling (ArXiv 2020)** 25 | - Jiawei Zhang 26 | - [[Paper]](https://arxiv.org/abs/2002.03283) 27 | - [[Python Reference]](https://github.com/jwzhanggy/SEG-BERT) 28 | 29 | - **Deep Graph Mapper: Seeing Graphs through the Neural Lens (ArXiv 2020)** 30 | - Cristian Bodnar, Cătălina Cangea, Pietro Liò 31 | - [[Paper]](https://arxiv.org/abs/2002.03864v2) 32 | - [[Python Reference]](https://github.com/crisbodnar/dgm) 33 | 34 | - **Benchmarking Graph Neural Networks (ArXiv 2020)** 35 | - Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, Xavier Bresson 36 | - [[Paper]](https://arxiv.org/abs/2003.00982) 37 | - [[Python Reference]](https://github.com/graphdeeplearning/benchmarking-gnns) 38 | 39 | - **Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction (BiorXiv 2020)** 40 | - Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen 41 | - [[Paper]](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0407-y) 42 | - [[Python Reference]](https://github.com/edvardlindelof/graph-neural-networks-for-drug-discovery) 43 | 44 | - **Second-Order Pooling for Graph Neural Networks (IEEE Transactions on Pattern Analysis and Machine Intelligence 2020)** 45 | - Zhengyang Wang, Shuiwang Ji 46 | - [[Paper]](https://ieeexplore.ieee.org/document/9104936) 47 | - [[Python Reference]](https://github.com/zhengyang-wang/sopool-gnns) 48 | 49 | - **Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport (ICLR 2020)** 50 | - Tengfei Ma, Jie Chen 51 | - [[Paper]](https://arxiv.org/abs/1912.11176v1) 52 | - [[Python Reference]](https://github.com/matenure/OTCoarsening) 53 | 54 | - **IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification (ICLR 2020)** 55 | - Lin Meng, Jiawei Zhang 56 | - [[Paper]](https://arxiv.org/abs/1907.09495v2) 57 | - [[Python Reference]](https://github.com/linmengsysu/IsoNN) 58 | 59 | - **Few-shot Learning on Graphs Via Super-Classes Based on Graph Spectral Measures (ICLR 2020)** 60 | - Jatin Chauhan, Deepak Nathani, Manohar Kaul 61 | - [[Paper]](https://openreview.net/forum?id=Bkeeca4Kvr) 62 | - [[Python Reference]](https://github.com/chauhanjatin10/GraphsFewShot) 63 | 64 | - **Memory-Based Graph Networks (ICLR 2020)** 65 | - Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, Quaid Morris 66 | - [[Paper]](https://openreview.net/forum?id=r1laNeBYPB) 67 | - [[Python Reference]](https://github.com/amirkhas/GraphMemoryNet) 68 | 69 | - **A Fair Comparison of Graph Neural Networks for Graph Classification (ICLR 2020)** 70 | - Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli 71 | - [[Paper]](https://openreview.net/pdf?id=HygDF6NFPB) 72 | - [[Python Reference]](https://github.com/diningphil/gnn-comparison) 73 | 74 | - **StructPool: Structured Graph Pooling via Conditional Random Fields (ICLR 2020)** 75 | - Hao Yuan, Shuiwang Ji 76 | - [[Paper]](https://openreview.net/forum?id=BJxg_hVtwH) 77 | - [[Python Reference]](https://github.com/Nate1874/StructPool) 78 | 79 | - **Strategies for Pre-training Graph Neural Networks (ICLR 2020)** 80 | - Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec 81 | - [[Paper]](https://arxiv.org/abs/1905.12265v3) 82 | - [[Python Reference]](https://github.com/snap-stanford/pretrain-gnns/) 83 | 84 | - **InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (ICLR 2020)** 85 | - Fan-yun Sun, Jordan Hoffman, Vikas Verma, Jian Tang 86 | - [[Paper]](https://openreview.net/pdf?id=r1lfF2NYvH) 87 | - [[Python Reference]](https://github.com/fanyun-sun/InfoGraph) 88 | 89 | - **Convolutional Kernel Networks for Graph-Structured Data (ICML 2020)** 90 | - Dexiong Chen, Laurent Jacob, Julien Mairal 91 | - [[Paper]](https://arxiv.org/abs/2003.05189v2) 92 | - [[Python Reference]](https://github.com/claying/GCKN) 93 | 94 | - **Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation (IJCAI 2020)** 95 | - Shuo Zhang, Lei Xie 96 | - [[Paper]](https://arxiv.org/abs/1907.02204) 97 | - [[Python Reference]](https://github.com/zetayue/CPA) 98 | 99 | - **Mutual Information Maximization in Graph Neural Networks (IJCNN 2020)** 100 | - Xinhan Di, Pengqian Yu, Rui Bu, Mingchao Sun 101 | - [[Paper]](https://arxiv.org/abs/1905.08509v4) 102 | - [[Python Reference]](https://github.com/CODE-SUBMIT/Graph_Neighborhood_1) 103 | 104 | ### 2019 105 | 106 | - **GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)** 107 | - Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang 108 | - [[Paper]](https://shiruipan.github.io/publication/aaai-2020-zhu) 109 | - [[Python Reference]](https://github.com/CheriseZhu/GSSNN) 110 | 111 | - **Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI 2019)** 112 | - Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe 113 | - [[Paper]](https://arxiv.org/pdf/1810.02244v2.pdf) 114 | - [[Python Reference]](https://github.com/k-gnn/k-gnn) 115 | 116 | - **DAGCN: Dual Attention Graph Convolutional Networks (ACPR 2019)** 117 | - Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long 118 | - [[Paper]](https://arxiv.org/abs/1904.02278v1) 119 | - [[Python Reference]](https://github.com/dawenzi123/DAGCN) 120 | 121 | - **Understanding Isomorphism Bias in Graph Data Sets (Arxiv 2019)** 122 | - Sergei Ivanov, Sergei Sviridov, Evgeny Burnaev 123 | - [[Paper]](https://arxiv.org/abs/1910.12091v2) 124 | - [[Python Reference]](https://github.com/nd7141/iso_bias) 125 | 126 | - **Graph Star Net for Generalized Multi-Task Learning (Arxiv 2019)** 127 | - Lu Haonan, Seth H. Huang, Tian Ye, Guo Xiuyan 128 | - [[Paper]](https://arxiv.org/abs/1906.12330v1) 129 | - [[Python Reference]](https://github.com/graph-star-team/graph_star) 130 | 131 | - **HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction (Arxiv 2019)** 132 | - Raehyun Kim, Chan Ho So, Minbyul Jeong, Sanghoon Lee, Jinkyu Kim, Jaewoo Kang 133 | - [[Paper]](https://arxiv.org/abs/1908.07999v3) 134 | - [[Python Reference]](https://github.com/dmis-lab/hats) 135 | 136 | - **Spectral Clustering with Graph Neural Networks for Graph Pooling (Arxiv 2019)** 137 | - Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi 138 | - [[Paper]](https://arxiv.org/abs/1907.00481v3) 139 | - [[Python Reference]](https://github.com/FilippoMB/Benchmark_dataset_for_graph_classification) 140 | 141 | - **Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)** 142 | - Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi 143 | - [[Paper]](https://arxiv.org/abs/1910.11436v1) 144 | - [[Python Reference]](https://github.com/danielegrattarola/decimation-pooling) 145 | 146 | - **Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)** 147 | - Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley 148 | - [[Paper]](https://arxiv.org/pdf/1902.08399v1.pdf) 149 | - [[Python Reference]](https://github.com/BraintreeLtd/PatchyCapsules) 150 | 151 | - **Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)** 152 | - Ting Chen, Song Bian, Yizhou Sun 153 | - [[Paper]](https://arxiv.org/abs/1905.04579v3) 154 | - [[Python Reference]](https://github.com/chentingpc/gfn) 155 | 156 | - **Universal Self-Attention Network for Graph Classification (Arxiv 2019)** 157 | - Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung} 158 | - [[Paper]](https://arxiv.org/abs/1909.11855) 159 | - [[Python Reference]](https://github.com/daiquocnguyen/Graph-Transformer) 160 | 161 | - **Discriminative Structural Graph Classification (ArXiV 2019)** 162 | - Younjoo Seo, Andreas Loukas, Nathanaël Perraudin 163 | - [[Paper]](https://arxiv.org/abs/1905.13422) 164 | - [[Python Reference]](https://github.com/youngjoo-epfl/DSGC) 165 | 166 | - **Symmetrical Graph Neural Network for Quantum Chemistry, with Dual R/K Space (ArXiV 2019)** 167 | - Shuqian Ye, Jiechun Liang, Rulin Liu, Xi Zhu 168 | - [[Paper]](https://arxiv.org/abs/1912.07256) 169 | - [[Python Reference]](https://github.com/yippp/SY-GNN) 170 | 171 | - **Graph Classification with Automatic Topologically-Oriented Learning (ArXiV 2019)** 172 | - Martin Royer, Frédéric Chazal, Clément Levrard, Yuichi Ike, Yuhei Umeda 173 | - [[Paper]](https://arxiv.org/pdf/1909.13472.pdf) 174 | - [[Python Reference]](https://github.com/martinroyer/atol) 175 | - [[Python]](https://github.com/giotto-ai/graph_classification_with_atol) 176 | 177 | - **Unsupervised Universal Self-Attention Network for Graph Classification (Arxiv 2019)** 178 | - Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phun 179 | - [[Paper]](https://arxiv.org/abs/1909.11855) 180 | - [[Python Reference]](https://github.com/daiquocnguyen/U2GNN) 181 | 182 | - **Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)** 183 | - Takenori Yamamoto 184 | - [[Paper]](https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf) 185 | - [[Python Reference]](https://github.com/Tony-Y/cgnn) 186 | 187 | - **Fast Training of Sparse Graph Neural Networks on Dense Hardware (Arxiv 2019)** 188 | - Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow 189 | - [[Paper]](https://arxiv.org/abs/1906.11786) 190 | - [[Python Reference]](https://github.com/anonymous-authors-iclr2020/fast_training_of_sparse_graph_neural_networks_on_dense_hardware) 191 | 192 | - **Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)** 193 | - Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi 194 | - [[Paper]](https://arxiv.org/abs/1910.11436) 195 | - [[Python Reference]](https://github.com/danielegrattarola/decimation-pooling) 196 | 197 | - **Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)** 198 | - Ting Chen, Song Bian, Yizhou Sun 199 | - [[Paper]](https://arxiv.org/abs/1905.04579) 200 | - [[Python Reference]](https://github.com/Waterpine/vis_network) 201 | 202 | - **K-hop Graph Neural Networks (Arxiv 2019)** 203 | - Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis 204 | - [[Paper]](https://arxiv.org/abs/1907.06051v1) 205 | - [[Python Reference]](https://github.com/giannisnik/k-hop-gnns) 206 | 207 | - **Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification (ECML-PKDD 2019)** 208 | - Lu Bai, Yuhang Jiao, Lixin Cui, Edwin R. Hancock 209 | - [[Paper]](https://arxiv.org/abs/1904.04238) 210 | - [[Python Reference]](https://github.com/baiuoy/ASGCN_ECML-PKDD2019) 211 | 212 | - **AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism (ICCV 2019)** 213 | - Jingjia Huang, Zhangheng Li, Nannan Li, Shan Liu, Ge Li 214 | - [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/html/Huang_AttPool_Towards_Hierarchical_Feature_Representation_in_Graph_Convolutional_Networks_via_ICCV_2019_paper.html) 215 | - [[Python Reference]](https://github.com/hjjpku/Attention_in_Graph) 216 | 217 | - **Variational Recurrent Neural Networks for Graph Classification (ICLR RLGM 2019)** 218 | - Edouard Pineau, Nathan de Lara 219 | - [[Paper]](https://arxiv.org/abs/1902.02721v4) 220 | - [[Python Reference]](https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification) 221 | 222 | - **edGNN: a Simple and Powerful GNN for Directed Labeled Graphs (ICLR RLGM 2019)** 223 | - Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani 224 | - [[Paper]](https://arxiv.org/abs/1904.08745v2) 225 | - [[Python Reference]](https://github.com/guillaumejaume/edGNN) 226 | 227 | - **Capsule Graph Neural Network (ICLR 2019)** 228 | - Zhang Xinyi and Lihui Chen 229 | - [[Paper]](https://openreview.net/forum?id=Byl8BnRcYm) 230 | - [[Python Reference]](https://github.com/benedekrozemberczki/CapsGNN) 231 | 232 | - **How Powerful are Graph Neural Networks? (ICLR 2019)** 233 | - Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka 234 | - [[Paper]](https://arxiv.org/abs/1810.00826) 235 | - [[Python Reference]](https://github.com/weihua916/powerful-gnns) 236 | 237 | - **Graph U-Nets (ICML 2019)** 238 | - Hongyang Gao, Shuiwang Ji 239 | - [[Paper]](https://arxiv.org/abs/1905.05178v1f) 240 | - [[Python Reference]](https://github.com/HongyangGao/Graph-U-Nets) 241 | 242 | - **Relational Pooling for Graph Representations (ICML 2019)** 243 | - Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro 244 | - [[Paper]](https://arxiv.org/abs/1903.02541) 245 | - [[Python Reference]](https://github.com/PurdueMINDS/RelationalPooling) 246 | 247 | - **IPC: A Benchmark Data Set for Learning with Graph-Structured Data (ICML LRGSD 2019)** 248 | - Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen, Michael Katz 249 | - [[Paper]](https://arxiv.org/abs/1905.06393) 250 | - [[Python Reference]](https://github.com/IBM/IPC-graph-data) 251 | 252 | - **Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structure (ICML 2019)** 253 | - Ruo-Chun Tzeng, Shan-Hung Wu 254 | - [[Paper]](http://proceedings.mlr.press/v97/tzeng19a/tzeng19a.pdf) 255 | - [[Python Reference]](https://github.com/rutzeng/EgoCNN) 256 | 257 | - **Self-Attention Graph Pooling (ICML 2019)** 258 | - Junhyun Lee, Inyeop Lee, Jaewoo Kang 259 | - [[Paper]](https://arxiv.org/abs/1904.08082) 260 | - [[Python Reference]](https://github.com/inyeoplee77/SAGPool) 261 | 262 | - **Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)** 263 | - Federico Baldassarre, Hossein Azizpour 264 | - [[Paper]](https://128.84.21.199/pdf/1905.13686.pdf) 265 | - [[Python Reference]](https://github.com/gn-exp/gn-exp) 266 | 267 | - **Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (IJCAI 2019)** 268 | - Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang 269 | - [[Paper]](https://arxiv.org/abs/1904.01098v2) 270 | - [[Python Reference]](https://github.com/yunshengb/UGraphEmb) 271 | 272 | - **Molecule Property Prediction Based on Spatial Graph Embedding (Journal of Cheminformatics Models 2019)** 273 | - Xiaofeng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei 274 | - [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00410) 275 | - [[Python Reference]](https://github.com/1128bian/C-SGEN) 276 | 277 | - **Graph Convolutional Networks with EigenPooling (KDD 2019)** 278 | - Yao Ma, Suhang Wang, Charu C Aggarwal, Jiliang Tang 279 | - [[Paper]](https://arxiv.org/pdf/1904.13107.pdf) 280 | - [[Python Reference]](https://github.com/alge24/eigenpooling) 281 | 282 | - **Distance Metric Learning for Graph Structured Data (KDD 2019)** 283 | - Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama 284 | - [[Paper]](https://arxiv.org/abs/2002.00727v1) 285 | - [[Python Reference]](https://github.com/birdwatcherYT/Learning-Interpretable-Metric-between-Graphs) 286 | 287 | - **Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (NeurIPS 2019)** 288 | - Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu 289 | - [[Paper]](https://arxiv.org/abs/1905.13192) 290 | - [[Python Reference]](https://github.com/KangchengHou/gntk) 291 | 292 | - **Provably Powerful Graph Networks (NeurIPS 2019)** 293 | - Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman 294 | - [[Paper]](https://arxiv.org/abs/1905.11136v4) 295 | - [[Python Reference]](https://github.com/hadarser/ProvablyPowerfulGraphNetworks_torch) 296 | 297 | - **Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction (NeurIPS 2019)** 298 | - Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson 299 | - [[Paper]](https://arxiv.org/abs/1802.05451) 300 | - [[Python Reference]](https://github.com/shikorab/SceneGraph) 301 | 302 | - **Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)** 303 | - Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang 304 | - [[Paper]](https://arxiv.org/pdf/1904.05003.pdf) 305 | - [[Python Reference]](https://github.com/benedekrozemberczki/SEAL-CI) 306 | 307 | ### 2018 308 | 309 | - **An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)** 310 | - Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen 311 | - [[Paper]](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf) 312 | - [[Python Tensorflow Reference]](https://github.com/muhanzhang/DGCNN) 313 | - [[Python Pytorch Reference]](https://github.com/muhanzhang/pytorch_DGCNN) 314 | - [[MATLAB Reference]](https://github.com/muhanzhang/DGCNN) 315 | - [[Python Alternative]](https://github.com/leftthomas/DGCNN) 316 | - [[Python Alternative]](https://github.com/hitlic/DGCNN-tensorflow) 317 | 318 | - **Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)** 319 | - Hyeoncheol Cho and Insung. S. Choi 320 | - [[Paper]](https://arxiv.org/abs/1811.09794) 321 | - [[Python Reference]](https://github.com/blackmints/3DGCN) 322 | 323 | - **Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)** 324 | - Yu Jin and Joseph F. JaJa 325 | - [[Paper]](https://arxiv.org/pdf/1805.07683v4.pdf) 326 | - [[Python Reference]](https://github.com/yuj-umd/graphRNN) 327 | 328 | - **Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (ArXiv 2018)** 329 | - Seongok Ryu, Jaechang Lim, and Woo Youn Kim 330 | - [[Paper]](https://arxiv.org/abs/1805.10988) 331 | - [[Python Reference]](https://github.com/SeongokRyu/Molecular-GAT) 332 | 333 | - **Edge Attention-based Multi-Relational Graph Convolutional Networks (ArXiv 2018)** 334 | - Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi 335 | - [[Paper]](https://arxiv.org/abs/1802.04944v1) 336 | - [[Python Reference]](https://github.com/Luckick/EAGCN) 337 | 338 | - **Compound-Protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)** 339 | - Masashi Tsubaki, Kentaro Tomii, and Jun Sese 340 | - [[Paper]](https://academic.oup.com/bioinformatics/article/35/2/309/5050020) 341 | - [[Python Reference]](https://github.com/masashitsubaki/CPI_prediction) 342 | - [[Python Reference]](https://github.com/masashitsubaki/GNN_molecules) 343 | - [[Python Alternative ]](https://github.com/xnuohz/GCNDTI) 344 | 345 | - **Machine Learning for Organic Cage Property Prediction (Chemical Matters 2018)** 346 | - Lukas Turcani, Rebecca Greenway, Kim Jelfs 347 | - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.chemmater.8b03572) 348 | - [[Python Reference]](https://github.com/qyuan7/Graph_Convolutional_Network_for_cages) 349 | 350 | - **Kernel Graph Convolutional Neural Networks (ICANN 2018)** 351 | - Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis 352 | - [[Paper]](https://arxiv.org/pdf/1710.10689.pdf) 353 | - [[Python Reference]](https://github.com/giannisnik/cnn-graph-classification) 354 | 355 | - **Residual Gated Graph ConvNets (ICLR 2018)** 356 | - Xavier Bresson and Thomas Laurent 357 | - [[Paper]](https://arxiv.org/pdf/1711.07553v2.pdf) 358 | - [[Python Pytorch Reference]](https://github.com/xbresson/spatial_graph_convnets) 359 | 360 | - **Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)** 361 | - Davide Bacciu, Federico Errica, and Alessio Micheli 362 | - [[Paper]](https://arxiv.org/pdf/1805.10636.pdf) 363 | - [[Python Reference]](https://github.com/diningphil/CGMM) 364 | 365 | - **MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)** 366 | - Nicola De Cao and Thomas Kipf 367 | - [[Paper]](https://arxiv.org/pdf/1805.11973.pdf) 368 | - [[Python Reference]](https://github.com/nicola-decao/MolGAN) 369 | 370 | - **Graph Capsule Convolutional Neural Networks (ICML 2018)** 371 | - Saurabh Verma and Zhi-Li Zhang 372 | - [[Paper]](https://arxiv.org/abs/1805.08090) 373 | - [[Python Reference]](https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks) 374 | 375 | - **Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)** 376 | - Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes 377 | - [[Paper]](https://ieeexplore.ieee.org/abstract/document/8545310) 378 | - [[Python Reference]](https://github.com/priba/siamese_ged) 379 | 380 | - **Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)** 381 | - Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu 382 | - [[Paper]](http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf) 383 | - [[Python Reference]](https://github.com/tuxchow/ccm) 384 | 385 | - **SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)** 386 | - Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller 387 | - [[Paper]](https://arxiv.org/abs/1807.02839) 388 | - [[Python Reference]](http://mott.in/publications/others/sgr/) 389 | 390 | - **Graph Classification Using Structural Attention (KDD 2018)** 391 | - John Boaz Lee, Ryan Rossi, and Xiangnan Kong 392 | - [[Paper]](http://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf) 393 | - [[Python Pytorch Reference]](https://github.com/benedekrozemberczki/GAM) 394 | 395 | - **Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)** 396 | - Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec 397 | - [[Paper]](https://arxiv.org/abs/1806.02473) 398 | - [[Python Reference]](https://github.com/bowenliu16/rl_graph_generation) 399 | 400 | - **Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)** 401 | - Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec 402 | - [[Paper]](http://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf) 403 | - [[Python Reference]](https://github.com/rusty1s/pytorch_geometric) 404 | 405 | - **Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters 2018)** 406 | - Masashi Tsubaki and Teruyasu Mizoguchi 407 | - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.jpclett.8b01837) 408 | - [[Python Reference]](https://github.com/masashitsubaki/molecularGNN_3Dstructure) 409 | 410 | ### 2017 411 | 412 | - **Semi-Supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (ArXiv 2017)** 413 | - Hai Nguyen, Shin-ichi Maeda, Kenta Oono 414 | - [[Paper]](https://arxiv.org/pdf/1711.10168.pdf) 415 | - [[Python Reference]](https://github.com/pfnet-research/hierarchical-molecular-learning) 416 | 417 | - **Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)** 418 | - Martin Simonovsky and Nikos Komodakis 419 | - [[paper]](https://arxiv.org/pdf/1704.02901v3.pdf) 420 | - [[Python Reference]](https://github.com/mys007/ecc) 421 | 422 | - **Graph Classification with 2D Convolutional Neural Networks (ICANN 2019)** 423 | - Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis 424 | - [[Paper]](https://arxiv.org/abs/1708.02218) 425 | - [[Python Reference]](https://github.com/Tixierae/graph_2D_CNN) 426 | 427 | - **Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)** 428 | - Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola 429 | - [[Paper]](https://arxiv.org/abs/1705.09037) 430 | - [[Python Reference]](https://github.com/taolei87/icml17_knn) 431 | 432 | - **CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)** 433 | - Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein 434 | - [[Paper]](https://arxiv.org/pdf/1705.07664v2.pdf) 435 | - [[Python Reference]](https://github.com/fmonti/CayleyNet) 436 | 437 | - **Deep Learning with Topological Signatures (NIPS 2017)** 438 | - Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl 439 | - [[paper]](https://arxiv.org/abs/1707.04041) 440 | - [[Python Reference]](https://github.com/c-hofer/nips2017) 441 | 442 | - **Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)** 443 | - Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur 444 | - [[Paper]](https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks) 445 | - [[Python Reference]](https://github.com/fouticus/pipgcn) 446 | 447 | ### 2016 448 | 449 | - **Gated Graph Sequence Neural Networks (ICLR 2016)** 450 | - Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel 451 | - [[Paper]](https://arxiv.org/abs/1511.05493) 452 | - [[Python TensorFlow]](https://github.com/bdqnghi/ggnn.tensorflow) 453 | - [[Python PyTorch]](https://github.com/JamesChuanggg/ggnn.pytorch) 454 | - [[Python Reference]](https://github.com/YunjaeChoi/ggnnmols) 455 | 456 | - **Learning Convolutional Neural Networks for Graphs (ICML 2016)** 457 | - Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov 458 | - [[Paper]](https://arxiv.org/abs/1605.05273) 459 | - [[Python Reference]](https://github.com/tvayer/PSCN) 460 | 461 | - **Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)** 462 | - Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough 463 | - [[Paper]](https://ieeexplore.ieee.org/document/7840988/) 464 | - [[Python Reference]](https://github.com/sbonner0/DeepTopologyClassification) 465 | 466 | - **Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)** 467 | - David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams 468 | - [[Paper]](https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf) 469 | - [[Python Reference]](https://github.com/fllinares/neural_fingerprints_tf) 470 | - [[Python Reference]](https://github.com/jacklin18/neural-fingerprint-in-GNN) 471 | - [[Python Reference]](https://github.com/HIPS/neural-fingerprint) 472 | - [[Python Reference]](https://github.com/debbiemarkslab/neural-fingerprint-theano) 473 | -------------------------------------------------------------------------------- /chapters/fingerprints.md: -------------------------------------------------------------------------------- 1 | ## Spectral and Statistical Fingerprints 2 | 3 | ### 2020 4 | 5 | - **Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models (CIKM 2020)** 6 | - Benedek Rozemberczki and Rik Sarkar 7 | - [[Paper]](https://arxiv.org/abs/2005.07959) 8 | - [[Python Karate Club]](https://github.com/benedekrozemberczki/karateclub/) 9 | - [[Python Reference]](https://github.com/benedekrozemberczki/FEATHER/) 10 | 11 | - **Explainable Classification of Brain Networks via Contrast Subgraphs (KDD 2020)** 12 | - Tommaso Lanciano, Francesco Bonchi, and Aristides Gionis 13 | - [[Paper]](https://dl.acm.org/doi/10.1145/3394486.3403383) 14 | - [[Python Reference]](https://github.com/tlancian/contrast-subgraph) 15 | 16 | - **Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs (WWW 2020)** 17 | - Anton Tsitsulin, Marina Munkhoeva, and Bryan Perozzi 18 | - [[Paper]](https://arxiv.org/abs/2003.01282) 19 | - [[Python Reference]](https://github.com/google-research/google-research/tree/master/graph_embedding/slaq) 20 | 21 | ### 2019 22 | 23 | - **A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)** 24 | - Chen Cai and Yusu Wang 25 | - [[Paper]](https://arxiv.org/abs/1811.03508) 26 | - [[Python Reference]](https://github.com/Chen-Cai-OSU/LDP) 27 | 28 | ### 2018 29 | 30 | - **Multi-Graph Multi-Label Learning Based on Entropy (Entropy 2018)** 31 | - Zixuan Zhu and Yuhai Zhao 32 | - [[Paper]](https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf) 33 | - [[Python Reference]](https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning) 34 | 35 | - **NetLSD: Hearing the Shape of a Graph (KDD 2018)** 36 | - Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller 37 | - [[Paper]](https://arxiv.org/abs/1805.10712) 38 | - [[Python Reference]](https://github.com/xgfs/NetLSD) 39 | - [[Python Karate Club]](https://github.com/benedekrozemberczki/karateclub/) 40 | 41 | - **A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning NIPS 2018)** 42 | - Nathan de Lara and Edouard Pineau 43 | - [[Paper]](https://arxiv.org/pdf/1810.09155.pdf) 44 | - [[Python Karate Club]](https://github.com/benedekrozemberczki/karateclub) 45 | - [[Python]](https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification) 46 | 47 | ### 2017 48 | 49 | - **Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)** 50 | - Saurabh Verma and Zhi-Li Zhang 51 | - [[Paper]](https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf) 52 | - [[Matlab Reference]](https://github.com/vermaMachineLearning/FGSD) 53 | - [[Python Karate Club]](https://github.com/benedekrozemberczki/karateclub/) 54 | 55 | ### 2015 56 | 57 | - **Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)** 58 | - Shirui Pan, Jia Wu, Xingquan Zhuy, Chengqi Zhang, and Philip S. Yuz 59 | - [[Paper]](https://ieeexplore.ieee.org/document/7302040) 60 | - [[Java Reference]](https://github.com/shiruipan/MTG) 61 | 62 | ### 2012 63 | 64 | - **NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)** 65 | - Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos 66 | - [[Paper]](https://arxiv.org/abs/1209.2684) 67 | - [[Python]](https://github.com/kristyspatel/Netsimile) 68 | -------------------------------------------------------------------------------- /chapters/kernels.md: -------------------------------------------------------------------------------- 1 | ## Graph Kernels 2 | 3 | ### 2019 4 | 5 | - **Distribution of Node Embeddings as Multiresolution Features for Graphs (ICDM 2019)** 6 | - Mark Heimann, Tara Safavi, and Danai Koutra 7 | - [[Paper]](https://gemslab.github.io/papers/heimann-2019-RGM.pdf) 8 | - [[Code]](https://github.com/GemsLab/RGM) 9 | 10 | - **Optimal Transport for Structured Data with Application on Graphs (ICML 2019)** 11 | - Vayer Titouan, Nicolas Courty, Romain Tavenard, Chapel Laetitia, Rémi Flamary 12 | - [[Paper]](http://proceedings.mlr.press/v97/titouan19a.html) 13 | - [[Python Reference]](https://github.com/PythonOT/POT) 14 | 15 | - **A Persistent Weisfeiler–Lehman Procedure for Graph Classification (ICML 2019)** 16 | - Sebastian Rieck, Christian Bock, and Karsten Borgwardt 17 | - [[Paper]](http://proceedings.mlr.press/v97/rieck19a/rieck19a.pdf) 18 | - [[Python Reference]](https://github.com/BorgwardtLab/P-WL) 19 | 20 | - **Wasserstein Weisfeiler-Lehman Graph Kernels (NIPS 2019)** 21 | - Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, and Karsten Borgwardt 22 | - [[Paper]](http://papers.nips.cc/paper/8872-wasserstein-weisfeiler-lehman-graph-kernels) 23 | - [[Code]](https://github.com/BorgwardtLab/WWL) 24 | 25 | - **Learning Metrics for Persistence-Based Summaries and Applications for Graph Classification (NIPS 2019)** 26 | - Qi Zhao, Yusu Wang 27 | - [[Paper]](https://arxiv.org/abs/1904.12189) 28 | - [[Code]](https://github.com/topology474/WKPI) 29 | 30 | - **Propagation Kernels: Efficient Graph Kernels from Propagated Information (Machine Learning 2019)** 31 | - Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting 32 | - [[Paper]](https://link.springer.com/article/10.1007/s10994-015-5517-9) 33 | - [[Matlab Reference]](https://github.com/marionmari/propagation_kernels) 34 | 35 | - **DDGK: Learning Graph Representations for Deep Divergence Graph Kernels (WWW 2019)** 36 | - Rami Al-Rfou, Dustin Zelle, Bryan Perozzi 37 | - [[Paper]](https://arxiv.org/abs/1904.09671v1) 38 | - [[Code]](https://github.com/google-research/google-research/tree/master/graph_embedding/ddgk) 39 | 40 | ### 2018 41 | 42 | - **Message Passing Graph Kernels (2018)** 43 | - Giannis Nikolentzos, Michalis Vazirgiannis 44 | - [[Paper]](https://arxiv.org/pdf/1808.02510.pdf) 45 | - [[Python Reference]](https://github.com/giannisnik/message_passing_graph_kernels) 46 | 47 | ### 2017 48 | 49 | - **Matching Node Embeddings for Graph Similarity (AAAI 2017)** 50 | - Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis 51 | - [[Paper]](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494) 52 | 53 | - **Global Weisfeiler-Lehman Graph Kernels (ICDM 2017)** 54 | - Christopher Morris, Kristian Kersting and Petra Mutzel 55 | - [[Paper]](https://arxiv.org/pdf/1703.02379.pdf) 56 | - [[C++ Reference]](https://github.com/chrsmrrs/glocalwl) 57 | 58 | - **Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor (JMLR 2017)** 59 | - Genki Kusano, Kenji Fukumizu, Yasuaki Hiraoka 60 | - [[Paper]](https://dl.acm.org/doi/abs/10.5555/3122009.3242046) 61 | - [[Python Reference]](https://github.com/genki-kusano/python-pwgk) 62 | 63 | - **On Valid Optimal Assignment Kernels and Applications to Graph Classification (NIPS 2016)** 64 | - Nils Kriege, Pierre-Louis Giscard, Richard Wilson 65 | - [[Paper]](https://arxiv.org/pdf/1606.01141.pdf) 66 | - [[Java Reference]](https://github.com/nlskrg/optimal_assignment_kernels) 67 | 68 | ### 2016 69 | 70 | - **Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)** 71 | - Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel 72 | - [[Paper]](https://arxiv.org/abs/1610.00064) 73 | - [[Python Reference]](https://github.com/chrsmrrs/hashgraphkernel) 74 | 75 | - **Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)** 76 | - Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian 77 | - [[Paper]](https://link.springer.com/article/10.1007/s10994-015-5517-9) 78 | - [[Matlab Reference]](https://github.com/marionmari/propagation_kernels) 79 | 80 | - **Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)** 81 | - Stephen Bonner, John Brennan, and A. Stephen McGough 82 | - [[Paper]](http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt) 83 | - [[python Reference]](https://github.com/sbonner0/GraphFingerprintComparison) 84 | 85 | - **The Multiscale Laplacian Graph Kernel (NIPS 2016)** 86 | - Risi Kondor and Horace Pan 87 | - [[Paper]](https://arxiv.org/abs/1603.06186) 88 | - [[C++ Reference]](https://github.com/horacepan/MLGkernel) 89 | 90 | ### 2015 91 | 92 | - **An Aligned Subtree Kernel for Weighted Graphs (ICML 2015)** 93 | - Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock 94 | - [[Paper]](http://proceedings.mlr.press/v37/bai15.pdf) 95 | 96 | - **A Graph Kernel Based on the Jensen-Shannon Representation Alignment (IJCAI 2015)** 97 | - Lu Bai, Zhihong Zhang, Chaoyan Wang, Xiao Bai, Edwin R. Hancock 98 | - [[Paper]](http://ijcai.org/Proceedings/15/Papers/468.pdf) 99 | - [[Matlab reference]](https://github.com/baiuoy/Matlab-code-JS-alignment-kernel-IJCAI-2015) 100 | 101 | - **Halting Random Walk Kernels (NIPS 2015)** 102 | - Mahito Sugiyama and Karsten M. Borgward 103 | - [[Paper]](https://pdfs.semanticscholar.org/79ba/8bcfbf9496834fdc22a1f7c96d26d776cd6c.pdf) 104 | - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels) 105 | 106 | ### 2013 107 | 108 | - **Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)** 109 | - Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt 110 | - [[Paper]](https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf) 111 | 112 | ### 2012 113 | 114 | - **Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)** 115 | - Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang 116 | - [[Paper]](https://ieeexplore.ieee.org/document/6413884/) 117 | - [[Python Reference]](https://github.com/benedekrozemberczki/NestedSubtreeHash) 118 | 119 | - **Subgraph Matching Kernels for Attributed Graphs (ICML 2012)** 120 | - Nils Kriege and Petra Mutzel 121 | - [[Paper]](https://arxiv.org/abs/1206.6483) 122 | - [[Python Reference]](https://github.com/mockingbird2/GraphKernelBenchmark) 123 | 124 | - **Two New Graphs Kernels in Chemoinformatics (Pattern Recognition Letters 2012)** 125 | - Benoit Gaüzère, LucBrun, and Didier Villemin 126 | - [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S016786551200102X) 127 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 128 | 129 | ### 2011 130 | 131 | - **Weisfeiler-Lehman Graph Kernels (JMLR 2011)** 132 | - Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt 133 | - [[Paper]](http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf) 134 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 135 | - [[Python Reference]](https://github.com/deeplego/wl-graph-kernels) 136 | - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels) 137 | 138 | ### 2010 139 | 140 | - **Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)** 141 | - Fabrizio Costa and Kurt De Grave 142 | - [[Paper]](https://icml.cc/Conferences/2010/papers/347.pdf) 143 | - [[C++ Reference]](www.bioinf.uni-freiburg.de/~costa/EDeNcpp.tgz) 144 | - [[Python Reference]](https://github.com/fabriziocosta/EDeN) 145 | 146 | - **Graph Kernels (JMLR 2010)** 147 | - S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt; 148 | - [[Paper]](http://www.jmlr.org/papers/volume11/vishwanathan10a/vishwanathan10a.pdf) 149 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 150 | 151 | ### 2009 152 | 153 | - **A Linear-time Graph Kernel (ICDM 2009)** 154 | - Shohei Hido and Hisashi Kashima 155 | - [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243) 156 | - [[Python Reference]](https://github.com/hgascon/adagio) 157 | 158 | - **Weisfeiler-Lehman Subtree Kernels (NIPS 2009)** 159 | - Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt 160 | - [[Paper]](http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf) 161 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 162 | - [[Python Reference]](https://github.com/deeplego/wl-graph-kernels) 163 | - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels) 164 | 165 | ### 2007 166 | 167 | - **Kernel on Bag of Paths For Measuring Similarity of Shapes (ESANN 2007)** 168 | - Frederic Suard, Alain Rakotomamonjy, and Abdelaziz Bensrhair 169 | - [[Paper]](https://pdfs.semanticscholar.org/149a/858889e8c3a54ee55b21511a7f56f5e9650b.pdf) 170 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 171 | 172 | ### 2005 173 | 174 | - **Fast Computation of Graph Kernels (NIPS 2006)** 175 | - S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph 176 | - [[Paper]](http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf) 177 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 178 | - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels) 179 | 180 | ### 2005 181 | 182 | - **Shortest-Path Kernels on Graphs (ICDM 2005)** 183 | - Karsten M. Borgwardt and Hans-Peter Kriegel 184 | - [[Paper]](https://www.ethz.ch/content/dam/ethz/special-interest/bsse/borgwardt-lab/documents/papers/BorKri05.pdf) 185 | - [[C++ Reference]](https://github.com/KitwareMedical/ITKTubeTK) 186 | 187 | - **Graph Kernels for Chemical Informatics (Neural Networks 2005)** 188 | - Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi 189 | - [[Paper]](https://www.sciencedirect.com/science/article/pii/S0893608005001693) 190 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 191 | 192 | ### 2004 193 | 194 | - **Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)** 195 | - Tamás Horváth, Thomas Gärtner, and Stefan Wrobel 196 | - [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158&rep=rep1&type=pdf) 197 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 198 | 199 | - **Extensions of Marginalized Graph Kernels (ICML 2004)** 200 | - Pierre Mahe , Nobuhisa Ueda , Tatsuya Akutsu , Jean-Luc Perret , Jean-Philippe Vert 201 | - [[Paper]](http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf) 202 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 203 | 204 | ### 2003 205 | 206 | - **Marginalized Kernels Between Labeled Graphs (ICML 2003)** 207 | - Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi 208 | - [[Paper]](https://pdfs.semanticscholar.org/2dfd/92c808487049ab4c9b45db77e9055b9da5a2.pdf) 209 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 210 | 211 | - **On Graph Kernels: Hardness Results and Efficient Alternatives (Learning Theory and Kernel Machines 2003)** 212 | - Thomas Gärtner, Peter Flach, and Stefan Wrobel 213 | - [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.8681&rep=rep1&type=pdf) 214 | - [[Python Reference]](https://github.com/jajupmochi/py-graph) 215 | -------------------------------------------------------------------------------- /chapters/matrix_factorization.md: -------------------------------------------------------------------------------- 1 | ## Matrix Factorization 2 | 3 | ### 2020 4 | 5 | - **Learning Distributed Representations of Graphs with Geo2DR (ICML GRL 2020)** 6 | - Paul Scherer and Pietro Lio 7 | - [[Paper]](https://arxiv.org/abs/2003.05926v3) 8 | - [[Python Reference]](https://github.com/paulmorio/geo2dr) 9 | 10 | ### 2019 11 | 12 | - **GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features (ICONIP 2019)** 13 | - Hong Chen, Hisashi Koga 14 | - [[Paper]](https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1) 15 | - [[Python Karate Club]](https://github.com/benedekrozemberczki/karateclub/) 16 | 17 | ### 2018 18 | 19 | - **Anonymous Walk Embeddings (ICML 2018)** 20 | - Sergey Ivanov and Evgeny Burnaev 21 | - [[Paper]](https://arxiv.org/pdf/1805.11921.pdf) 22 | - [[Python Reference]](https://github.com/nd7141/AWE) 23 | 24 | - **Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition (Pattern Recognition 2018)** 25 | - Anjan Dutta, Pau Riba, Josep Lladós, Alicia Fornés 26 | - [[Paper]](https://arxiv.org/abs/1807.02839) 27 | - [[Matlab Reference]](https://github.com/priba/hierarchicalSGE) 28 | 29 | - **Learning Graph Representation via Frequent Subgraphs (SDM 2018)** 30 | - Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung 31 | - [[Paper]](https://epubs.siam.org/doi/10.1137/1.9781611975321.35) 32 | - [[Python Reference]](https://github.com/nphdang/GE-FSG) 33 | 34 | ### 2017 35 | 36 | - **Graph2vec (MLGWorkshop 2017)** 37 | - Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan 38 | - [[Paper]](https://arxiv.org/abs/1707.05005) 39 | - [[Python Karate Club]](https://github.com/benedekrozemberczki/karateclub/) 40 | - [[Python High Performance]](https://github.com/benedekrozemberczki/graph2vec) 41 | - [[Python Reference]](https://github.com/MLDroid/graph2vec_tf) 42 | 43 | ### 2016 44 | 45 | - **Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)** 46 | - Petar Ristoski and Heiko Paulheim 47 | - [[Paper]](https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30) 48 | - [[Python Reference]](https://github.com/airobert/RDF2VecAtWebScale) 49 | 50 | - **Subgraph2Vec (MLGWorkshop 2016)** 51 | - Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan 52 | - [[Paper]](https://arxiv.org/abs/1606.08928) 53 | - [[Python High Performance]](https://github.com/MLDroid/subgraph2vec_gensim) 54 | - [[Python Reference]](https://github.com/MLDroid/subgraph2vec_tf) 55 | 56 | ### 2015 57 | 58 | - **Deep Graph Kernels (KDD 2015)** 59 | - Pinar Yanardag and S.V.N. Vishwanathan 60 | - [[Paper]](https://dl.acm.org/citation.cfm?id=2783417) 61 | - [[Python Reference]](https://github.com/pankajk/Deep-Graph-Kernels) 62 | -------------------------------------------------------------------------------- /code-of-conduct.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, gender identity and expression, level of experience, 9 | nationality, personal appearance, race, religion, or sexual identity and 10 | orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies both within project spaces and in public spaces 49 | when an individual is representing the project or its community. Examples of 50 | representing a project or community include using an official project e-mail 51 | address, posting via an official social media account, or acting as an appointed 52 | representative at an online or offline event. Representation of a project may be 53 | further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at benedek.rozemberczki@gmail.com. All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at [http://contributor-covenant.org/version/1/4][version] 72 | 73 | [homepage]: http://contributor-covenant.org 74 | [version]: http://contributor-covenant.org/version/1/4/ 75 | -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | # Contribution Guidelines 2 | 3 | Please note that this project is released with a [Contributor Code of Conduct](code-of-conduct.md). By participating in this project you agree to abide by its terms. 4 | 5 | The pull request should have a useful title. Pull requests with `Update readme.md` as title will be closed. Please carefully read everything in `Adding to this list`. 6 | 7 | ## Adding to this list 8 | 9 | Please ensure your pull request adheres to the following guidelines: 10 | 11 | - Search previous suggestions before making a new one, as yours may be a duplicate. 12 | - Make an individual pull request for each suggestion. 13 | - Chose corresponding section (Factorization, Deep Learning and so on) for your suggestion. 14 | - Include the name of the paper. 15 | - Include the year and conference in which the paper came out. 16 | - Keep chronological order. 17 | - Add the paper authors. 18 | - Add a link to the paper - preferrably on ArXiv. 19 | - Add an implementation of the paper. You can add multiple implementations. 20 | - Check your spelling and grammar. 21 | - List, after your addition, should be alphabetically. 22 | - The pull request and commit should have a useful title. 23 | - The body of your commit message should contain a link to the repository. 24 | 25 | Thank you for your suggestions! 26 | --------------------------------------------------------------------------------