{"id":34912,"url":"https://github.com/eric-erki/awesome-graph-classification","name":"awesome-graph-classification","description":"A collection of important graph embedding, classification and representation learning papers with implementations.","projects_count":211,"last_synced_at":"2026-06-05T20:00:25.385Z","repository":{"id":99090949,"uuid":"237751366","full_name":"eric-erki/awesome-graph-classification","owner":"eric-erki","description":"A collection of important graph embedding, classification and representation learning papers with 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Learning","Factorization","Spectral and Statistical Fingerprints","Graph Kernels"],"sub_categories":[],"readme":"# Awesome Graph Classification\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n![GitHub stars](https://img.shields.io/github/stars/benedekrozemberczki/awesome-graph-embedding.svg?style=plastic)\n![GitHub forks](https://img.shields.io/github/forks/benedekrozemberczki/awesome-graph-embedding.svg?color=blue\u0026style=plastic)\n![License](https://img.shields.io/github/license/benedekrozemberczki/awesome-graph-embedding.svg?color=blue\u0026style=plastic)\n\nA collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations.\n\nRelevant graph classification benchmark datasets are available [[here]](https://github.com/shiruipan/graph_datasets).\n\nSimilar collections about [community detection](https://github.com/benedekrozemberczki/awesome-community-detection), [classification/regression tree](https://github.com/benedekrozemberczki/awesome-decision-tree-papers), [fraud detection](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers), [Monte Carlo tree search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers), and [gradient boosting](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) papers with implementations.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"400\" src=\"atlas.png\"\u003e\n\u003c/p\u003e\n\n##### Contents  \n\n1. [Factorization](#factorization)  \n2. [Spectral and Statistical Fingerprints](#spectral-and-statistical-fingerprints)\n3. [Deep Learning](#deep-learning)  \n4. [Graph Kernels](#graph-kernels)  \n\n## Factorization\n\n- **GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features (ICONIP 2019)**\n  - Hong Chen, Hisashi Koga\n  - [[Paper]](https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1)\n  - [[Python Reference]](https://github.com/benedekrozemberczki/karateclub/)\n\n- **Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition (Pattern Recognition 2018)**\n  - Anjan Dutta, Pau Riba, Josep Lladós, Alicia Fornés\n  - [[Paper]](https://arxiv.org/abs/1807.02839)\n  - [[Matlab Reference]](https://github.com/priba/hierarchicalSGE)\n  \n- **Learning Graph Representation via Frequent Subgraphs (SDM 2018)**\n  - Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung\n  - [[Paper]](https://epubs.siam.org/doi/10.1137/1.9781611975321.35)\n  - [[Python Reference]](https://github.com/nphdang/GE-FSG)\n\n- **Anonymous Walk Embeddings (ICML 2018)**\n  - Sergey Ivanov and Evgeny Burnaev\n  - [[Paper]](https://arxiv.org/pdf/1805.11921.pdf)\n  - [[Python Reference]](https://github.com/nd7141/AWE)\n\n- **Graph2vec (MLGWorkshop 2017)**\n  - Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan\n  - [[Paper]](https://arxiv.org/abs/1707.05005)\n  - [[Python High Performance]](https://github.com/benedekrozemberczki/graph2vec)\n  - [[Python Reference]](https://github.com/MLDroid/graph2vec_tf)\n\n- **Subgraph2Vec (MLGWorkshop 2016)**\n  - Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan\n  - [[Paper]](https://arxiv.org/abs/1606.08928)\n  - [[Python High Performance]](https://github.com/MLDroid/subgraph2vec_gensim)\n  - [[Python Reference]](https://github.com/MLDroid/subgraph2vec_tf)\n\n- **Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)**\n  - Petar Ristoski and Heiko Paulheim\n  - [[Paper]](https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30)\n  - [[Python Reference]](https://github.com/airobert/RDF2VecAtWebScale)\n\n- **Deep Graph Kernels (KDD 2015)**\n  - Pinar Yanardag and S.V.N. Vishwanathan\n  - [[Paper]](https://dl.acm.org/citation.cfm?id=2783417)\n  - [[Python Reference]](https://github.com/pankajk/Deep-Graph-Kernels)\n\n## Spectral and Statistical Fingerprints\n\n- **A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)**\n  - Chen Cai, Yusu Wang\n  - [[Paper]](https://arxiv.org/abs/1811.03508)\n  - [[Python Reference]](https://github.com/Chen-Cai-OSU/LDP)\n\n- **NetLSD (KDD 2018)**\n  - Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller\n  - [[Paper]](https://arxiv.org/abs/1805.10712)\n  - [[Python Reference]](https://github.com/xgfs/NetLSD)\n\n- **A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)**\n  - Nathan de Lara and Edouard Pineau\n  - [[Paper]](https://arxiv.org/pdf/1810.09155.pdf)\n  - [[Python]](https://karateclub.readthedocs.io/)\n  - [[Python]](https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification)\n\n- **Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)**\n  - Zixuan Zhu and Yuhai Zhao\n  - [[Paper]](https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf)\n  - [[Python Reference]](https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning)\n\n- **Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)**\n  - Saurabh Verma and Zhi-Li Zhang\n  - [[Paper]](https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf)\n  - [[Matlab Reference]](https://github.com/vermaMachineLearning/FGSD)\n  - [[Python Reference]](https://github.com/benedekrozemberczki/karateclub/)\n\n- **Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)**\n  - Shirui Pan, Jia Wu, Xingquan Zhuy, Chengqi Zhang, and Philip S. Yuz\n  - [[Paper]](https://ieeexplore.ieee.org/document/7302040)\n  - [[Java Reference]](https://github.com/shiruipan/MTG)\n\n- **NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)**\n  - Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos\n  - [[Paper]](https://arxiv.org/abs/1209.2684)\n  - [[Python]](https://github.com/kristyspatel/Netsimile)\n\n## Deep Learning\n\n- **Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction (AAAI 2020)**\n  - Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen\n  - [[Paper]](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0407-y)\n  - [[Python Reference]](https://github.com/edvardlindelof/graph-neural-networks-for-drug-discovery)\n  \n- **GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)**\n  - Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang \n  - [[Paper]](https://shiruipan.github.io/publication/aaai-2020-zhu)\n  - [[Python Reference]](https://github.com/CheriseZhu/GSSNN)\n  \n- **Graph Convolutional Networks with EigenPooling (KDD 2019)**\n  - Yao Ma, Suhang Wang, Charu C Aggarwal, Jiliang Tang\n  - [[Paper]](https://arxiv.org/pdf/1904.13107.pdf)\n  - [[Python Reference]](https://github.com/alge24/eigenpooling)\n\n- **Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (NeurIPS 2019)**\n  - Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu\n  - [[Paper]](https://arxiv.org/abs/1905.13192)\n  - [[Python Reference]](https://github.com/KangchengHou/gntk)\n\n- **Molecule Property Prediction Based on Spatial Graph Embedding (Journal of Cheminformatics Models 2019)**\n  - Xiaofeng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei\n  - [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00410)\n  - [[Python Reference]](https://github.com/1128bian/C-SGEN)\n  \n- **Unsupervised Universal Self-Attention Network for Graph Classification (Arxiv 2019)**\n  - Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phun\n  - [[Paper]](https://arxiv.org/abs/1909.11855)\n  - [[Python Reference]](https://github.com/daiquocnguyen/U2GNN)\n\n- **Fast Training of Sparse Graph Neural Networks on Dense Hardware (Arxiv 2019)**\n  - Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow\n  - [[Paper]](https://arxiv.org/abs/1906.11786)\n  - [[Python Reference]](https://github.com/anonymous-authors-iclr2020/fast_training_of_sparse_graph_neural_networks_on_dense_hardware)\n  \n- **Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)**\n  - Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi\n  - [[Paper]](https://arxiv.org/abs/1910.11436)\n  - [[Python Reference]](https://github.com/danielegrattarola/decimation-pooling)\n  \n- **Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)**\n  - Ting Chen, Song Bian, Yizhou Sun\n  - [[Paper]](https://arxiv.org/abs/1905.04579)\n  - [[Python Reference]](https://github.com/Waterpine/vis_network) \n\n- **Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification (ECML-PKDD 2019)**\n  - Lu Bai, Yuhang Jiao, Lixin Cui, Edwin R. Hancock\n  - [[Paper]](https://arxiv.org/abs/1904.04238)\n  - [[Python Reference]](https://github.com/baiuoy/ASGCN_ECML-PKDD2019) \n\n- **Relational Pooling for Graph Representations (ICML 2019)**\n  - Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro\n  - [[Paper]](https://arxiv.org/abs/1903.02541)\n  - [[Python Reference]](https://github.com/PurdueMINDS/RelationalPooling)\n\n- **Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structure (ICML 2019)**\n  - Ruo-Chun Tzeng, Shan-Hung Wu\n  - [[Paper]](http://proceedings.mlr.press/v97/tzeng19a/tzeng19a.pdf)\n  - [[Python Reference]](https://github.com/rutzeng/EgoCNN)\n\n- **Self-Attention Graph Pooling (ICML 2019)**\n  - Junhyun Lee, Inyeop Lee, Jaewoo Kang\n  - [[Paper]](https://arxiv.org/abs/1904.08082)\n  - [[Python Reference]](https://github.com/inyeoplee77/SAGPool)\n\n- **Variational Recurrent Neural Networks for Graph Classification (ICLR 2019)**\n  - Edouard Pineau, Nathan de Lara\n  - [[Paper]](https://arxiv.org/abs/1902.02721)\n  - [[Python Reference]](https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification)\n\n- **Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)**\n  - Takenori Yamamoto\n  - [[Paper]](https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf)\n  - [[Python Reference]](https://github.com/Tony-Y/cgnn)\n\n- **Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)**\n  - Federico Baldassarre, Hossein Azizpour\n  - [[Paper]](https://128.84.21.199/pdf/1905.13686.pdf)\n  - [[Python Reference]](https://github.com/gn-exp/gn-exp)\n\n- **Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)**\n  - Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang\n  - [[Paper]](https://arxiv.org/pdf/1904.05003.pdf)\n  - [[Python Reference]](https://github.com/benedekrozemberczki/SEAL-CI)\n\n- **Capsule Graph Neural Network (ICLR 2019)**\n  - Zhang Xinyi and Lihui Chen\n  - [[Paper]](https://openreview.net/forum?id=Byl8BnRcYm)\n  - [[Python Reference]](https://github.com/benedekrozemberczki/CapsGNN)\n\n- **How Powerful are Graph Neural Networks? (ICLR 2019)**\n  - Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka\n  - [[Paper]](https://arxiv.org/abs/1810.00826)\n  - [[Python Reference]](https://github.com/weihua916/powerful-gnns)\n\n- **Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI 2019)**\n  - Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe\n  - [[Paper]](https://arxiv.org/pdf/1810.02244v2.pdf)\n  - [[Python Reference]](https://github.com/k-gnn/k-gnn)\n\n- **Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)**\n  - Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley\n  - [[Paper]](https://arxiv.org/pdf/1902.08399v1.pdf)\n  - [[Python Reference]](https://github.com/BraintreeLtd/PatchyCapsules)\n  \n- **Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction (NIPS 2019)**\n  - Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson\n  - [[Paper]](https://arxiv.org/abs/1802.05451)\n  - [[Python Reference]](https://github.com/shikorab/SceneGraph)\n  \n- **Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters 2018)**\n  - Masashi Tsubaki and Teruyasu Mizoguchi\n  - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.jpclett.8b01837)\n  - [[Python Reference]](https://github.com/masashitsubaki/molecularGNN_3Dstructure)\n  \n- **Machine Learning for Organic Cage Property Prediction (Chemical Matters 2018)**\n  - Lukas Turcani, Rebecca Greenway, Kim Jelfs\n  - [[Paper]](https://pubs.acs.org/doi/10.1021/acs.chemmater.8b03572)\n  - [[Python Reference]](https://github.com/qyuan7/Graph_Convolutional_Network_for_cages)\n  \n- **Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)**\n  - Hyeoncheol Cho and Insung. S. Choi\n  - [[Paper]](https://arxiv.org/abs/1811.09794)\n  - [[Python Reference]](https://github.com/blackmints/3DGCN)\n\n- **Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)**\n  - Yu Jin and Joseph F. JaJa\n  - [[Paper]](https://arxiv.org/pdf/1805.07683v4.pdf)\n  - [[Python Reference]](https://github.com/yuj-umd/graphRNN)\n\n- **Graph Capsule Convolutional Neural Networks (ICML 2018)**\n  - Saurabh Verma and Zhi-Li Zhang\n  - [[Paper]](https://arxiv.org/abs/1805.08090)\n  - [[Python Reference]](https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks)\n\n- **Graph Classification Using Structural Attention (KDD 2018)**\n  - John Boaz Lee, Ryan Rossi, and Xiangnan Kong\n  - [[Paper]](http://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf)\n  - [[Python Pytorch Reference]](https://github.com/benedekrozemberczki/GAM)\n\n- **Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)**\n  - Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec\n  - [[Paper]](https://arxiv.org/abs/1806.02473)\n  - [[Python Reference]](https://github.com/bowenliu16/rl_graph_generation)\n\n- **Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)**\n  - Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec\n  - [[Paper]](http://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf)\n  - [[Python Reference]](https://github.com/rusty1s/pytorch_geometric)\n\n- **Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)**\n  - Davide Bacciu, Federico Errica, and Alessio Micheli\n  - [[Paper]](https://arxiv.org/pdf/1805.10636.pdf)\n  - [[Python Reference]](https://github.com/diningphil/CGMM)\n\n- **MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)**\n  - Nicola De Cao and Thomas Kipf\n  - [[Paper]](https://arxiv.org/pdf/1805.11973.pdf)\n  - [[Python Reference]](https://github.com/nicola-decao/MolGAN)\n\n- **Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)**\n  - Seongok Ryu, Jaechang Lim, and Woo Youn Kim    \n  - [[Paper]](https://arxiv.org/abs/1805.10988)\n  - [[Python Reference]](https://github.com/SeongokRyu/Molecular-GAT)\n\n- **Compound-Protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)**\n  - Masashi Tsubaki, Kentaro Tomii, and Jun Sese\n  - [[Paper]](https://academic.oup.com/bioinformatics/article/35/2/309/5050020)\n  - [[Python Reference]](https://github.com/masashitsubaki/CPI_prediction)\n  - [[Python Reference]](https://github.com/masashitsubaki/GNN_molecules)\n  - [[Python Alternative ]](https://github.com/xnuohz/GCNDTI)\n\n- **Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)**\n  - Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes\n  - [[Paper]](https://ieeexplore.ieee.org/abstract/document/8545310)\n  - [[Python Reference]](https://github.com/priba/siamese_ged)\n\n- **Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)**\n  - Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi  \n  - [[Paper]](https://arxiv.org/abs/1802.04944v1)\n  - [[Python Reference]](https://github.com/Luckick/EAGCN)\n\n- **Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)**\n  - Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu\n  - [[Paper]](http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf)\n  - [[Python Reference]](https://github.com/tuxchow/ccm)\n\n- **Residual Gated Graph ConvNets (ICLR 2018)**\n  - Xavier Bresson and Thomas Laurent\n  - [[Paper]](https://arxiv.org/pdf/1711.07553v2.pdf)\n  - [[Python Pytorch Reference]](https://github.com/xbresson/spatial_graph_convnets)\n\n- **An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)**\n  - Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen\n  - [[Paper]](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)\n  - [[Python Tensorflow Reference]](https://github.com/muhanzhang/DGCNN)    \n  - [[Python Pytorch Reference]](https://github.com/muhanzhang/pytorch_DGCNN)\n  - [[MATLAB Reference]](https://github.com/muhanzhang/DGCNN)\n  - [[Python Alternative]](https://github.com/leftthomas/DGCNN)\n  - [[Python Alternative]](https://github.com/hitlic/DGCNN-tensorflow)\n\n- **SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)**\n  - Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller\n  - [[Paper]](https://arxiv.org/abs/1807.02839)\n  - [[Python Reference]](http://mott.in/publications/others/sgr/)\n\n- **Deep Learning with Topological Signatures (NIPS 2017)**\n  - Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl\n  - [[paper]](https://arxiv.org/abs/1707.04041)\n  - [[Python Reference]](https://github.com/c-hofer/nips2017)\n\n- **Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)**\n  - Martin Simonovsky and Nikos Komodakis\n  - [[paper]](https://arxiv.org/pdf/1704.02901v3.pdf)\n  - [[Python Reference]](https://github.com/mys007/ecc)\n\n- **Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)**\n  - Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola\n  - [[Paper]](https://arxiv.org/abs/1705.09037)\n  - [[Python Reference]](https://github.com/taolei87/icml17_knn)\n\n- **Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)**\n  - Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur\n  - [[Paper]](https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks)\n  - [[Python Reference]](https://github.com/fouticus/pipgcn)\n\n- **Graph Classification with 2D Convolutional Neural Networks (2017)**\n  - Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis\n  - [[Paper]](https://arxiv.org/abs/1708.02218)\n  - [[Python Reference]](https://github.com/Tixierae/graph_2D_CNN)\n\n- **CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)**\n  - Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein\n  - [[Paper]](https://arxiv.org/pdf/1705.07664v2.pdf)\n  - [[Python Reference]](https://github.com/fmonti/CayleyNet)\n\n- **Semi-Supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)**\n  - Hai Nguyen, Shin-ichi Maeda, Kenta Oono\n  - [[Paper]](https://arxiv.org/pdf/1711.10168.pdf)\n  - [[Python Reference]](https://github.com/pfnet-research/hierarchical-molecular-learning)\n\n- **Kernel Graph Convolutional Neural Networks (2017)**\n  - Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis\n  - [[Paper]](https://arxiv.org/pdf/1710.10689.pdf)\n  - [[Python Reference]](https://github.com/giannisnik/cnn-graph-classification)\n\n- **Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)**\n  - Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough\n  - [[Paper]](https://ieeexplore.ieee.org/document/7840988/)\n  - [[Python Reference]](https://github.com/sbonner0/DeepTopologyClassification)\n\n- **Learning Convolutional Neural Networks for Graphs (ICML 2016)**\n  - Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov\n  - [[Paper]](https://arxiv.org/abs/1605.05273)\n  - [[Python Reference]](https://github.com/tvayer/PSCN)\n\n- **Gated Graph Sequence Neural Networks (ICLR 2016)**\n  - Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel\n  - [[Paper]](https://arxiv.org/abs/1511.05493)\n  - [[Python TensorFlow]](https://github.com/bdqnghi/ggnn.tensorflow)\n  - [[Python PyTorch]](https://github.com/JamesChuanggg/ggnn.pytorch)\n  - [[Python Reference]](https://github.com/YunjaeChoi/ggnnmols)\n\n- **Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)**\n  - David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams\n  - [[Paper]](https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf)\n  - [[Python Reference]](https://github.com/fllinares/neural_fingerprints_tf)\n  - [[Python Reference]](https://github.com/jacklin18/neural-fingerprint-in-GNN)\n  - [[Python Reference]](https://github.com/HIPS/neural-fingerprint)\n  - [[Python Reference]](https://github.com/debbiemarkslab/neural-fingerprint-theano)\n\n## Graph Kernels\n\n- **A Persistent Weisfeiler–Lehman Procedure for Graph Classification (ICML 2019)**\n  - Sebastian Rieck, Christian Bock, and Karsten Borgwardt\n  - [[Paper]](http://proceedings.mlr.press/v97/rieck19a/rieck19a.pdf)\n  - [[Python Reference]](https://github.com/BorgwardtLab/P-WL)\n\n- **Message Passing Graph Kernels (2018)**\n  - Giannis Nikolentzos, Michalis Vazirgiannis\n  - [[Paper]](https://arxiv.org/pdf/1808.02510.pdf)\n  - [[Python Reference]](https://github.com/giannisnik/message_passing_graph_kernels)\n\n- **Matching Node Embeddings for Graph Similarity (AAAI 2017)**\n  - Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis\n  - [[Paper]](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494)\n\n- **Global Weisfeiler-Lehman Graph Kernels (2017)**\n  - Christopher Morris, Kristian Kersting and Petra Mutzel\n  - [[Paper]](https://arxiv.org/pdf/1703.02379.pdf)\n  - [[C++ Reference]](https://github.com/chrsmrrs/glocalwl)\n\n- **On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)**\n  - Nils Kriege, Pierre-Louis Giscard, Richard Wilson\n  - [[Paper]](https://arxiv.org/pdf/1606.01141.pdf)\n  - [[Java Reference]](https://github.com/nlskrg/optimal_assignment_kernels)\n\n- **Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)**\n  - Stephen Bonner, John Brennan, and A. Stephen McGough\n  - [[Paper]](http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt)    \n  - [[python Reference]](https://github.com/sbonner0/GraphFingerprintComparison)\n\n- **The Multiscale Laplacian Graph Kernel (NIPS 2016)**\n  - Risi Kondor and Horace Pan\n  - [[Paper]](https://arxiv.org/abs/1603.06186)\n  - [[C++ Reference]](https://github.com/horacepan/MLGkernel)\n\n- **Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)**\n  - Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel\n  - [[Paper]](https://arxiv.org/abs/1610.00064)\n  - [[Python Reference]](https://github.com/chrsmrrs/hashgraphkernel)\n\n- **Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)**\n  - Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian\n  - [[Paper]](https://link.springer.com/article/10.1007/s10994-015-5517-9)\n  - [[Matlab Reference]](https://github.com/marionmari/propagation_kernels)\n\n- **Halting Random Walk Kernels (NIPS 2015)**\n  - Mahito Sugiyama and Karsten M. Borgward\n  - [[Paper]](https://pdfs.semanticscholar.org/79ba/8bcfbf9496834fdc22a1f7c96d26d776cd6c.pdf)\n  - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)\n  \n- **A Graph Kernel Based on the Jensen-Shannon Representation Alignment (IJCAI 2015)**\n  - Lu Bai, Zhihong Zhang, Chaoyan Wang, Xiao Bai, Edwin R. Hancock\n  - [[Paper]](http://ijcai.org/Proceedings/15/Papers/468.pdf)\n  - [[Matlab reference]](https://github.com/baiuoy/Matlab-code-JS-alignment-kernel-IJCAI-2015)\n\n- **An Aligned Subtree Kernel for Weighted Graphs (ICML 2015)**\n  - Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock\n  - [[Paper]](http://proceedings.mlr.press/v37/bai15.pdf)\n\n- **Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)**\n  - Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt\n  - [[Paper]](https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf)\n\n- **Subgraph Matching Kernels for Attributed Graphs (ICML 2012)**\n  - Nils Kriege and Petra Mutzel\n  - [[Paper]](https://arxiv.org/abs/1206.6483)\n  - [[Python Reference]](https://github.com/mockingbird2/GraphKernelBenchmark)  \n\n- **Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)**\n  - Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang\n  - [[Paper]](https://ieeexplore.ieee.org/document/6413884/)\n  - [[Python Reference]](https://github.com/benedekrozemberczki/NestedSubtreeHash)  \n\n- **Weisfeiler-Lehman Graph Kernels (JMLR 2011)**\n  - Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt\n  - [[Paper]](http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n  - [[Python Reference]](https://github.com/deeplego/wl-graph-kernels)\n  - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)  \n  \n- **Two New Graphs Kernels in Chemoinformatics (Pattern Recognition Letters 2012)**\n  - Benoit Gaüzère, LucBrun, and Didier Villemin\n  - [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S016786551200102X)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n- **Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)**\n  - Fabrizio Costa and Kurt De Grave\n  - [[Paper]](https://icml.cc/Conferences/2010/papers/347.pdf)\n  - [[C++ Reference]](www.bioinf.uni-freiburg.de/~costa/EDeNcpp.tgz)\n  - [[Python Reference]](https://github.com/fabriziocosta/EDeN)\n  \n- **Graph Kernels (JMLR 2010)**\n  - S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt;\n  - [[Paper]](http://www.jmlr.org/papers/volume11/vishwanathan10a/vishwanathan10a.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n- **A Linear-time Graph Kernel (ICDM 2009)**\n  - Shohei Hido and Hisashi Kashima\n  - [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243)  \n  - [[Python Reference]](https://github.com/hgascon/adagio)\n\n- **Weisfeiler-Lehman Subtree Kernels (NIPS 2009)**\n  - Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt\n  - [[Paper]](http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n  - [[Python Reference]](https://github.com/deeplego/wl-graph-kernels)\n  - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)\n  \n- **Kernel on Bag of Paths For Measuring Similarity of Shapes (InESANN 2007)**\n  - Frederic Suard, Alain Rakotomamonjy, and Abdelaziz Bensrhair\n  - [[Paper]](https://pdfs.semanticscholar.org/149a/858889e8c3a54ee55b21511a7f56f5e9650b.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n- **Fast Computation of Graph Kernels (NIPS 2006)**\n  - S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph\n  - [[Paper]](http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n  - [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)\n\n- **Shortest-Path Kernels on Graphs (ICDM 2005)**\n  - Karsten M. Borgwardt and Hans-Peter Kriegel\n  - [[Paper]](https://www.ethz.ch/content/dam/ethz/special-interest/bsse/borgwardt-lab/documents/papers/BorKri05.pdf)\n  - [[C++ Reference]](https://github.com/KitwareMedical/ITKTubeTK)\n  \n- **Graph Kernels for Chemical Informatics (Neural Networks 2005)**\n  - Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi\n  - [[Paper]](https://www.sciencedirect.com/science/article/pii/S0893608005001693)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n- **Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)**\n  - Tamás Horváth, Thomas Gärtner, and Stefan Wrobel\n  - [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158\u0026rep=rep1\u0026type=pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n- **Extensions of Marginalized Graph Kernels (ICML 2004)**\n  - Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert\n  - [[Paper]](http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n  \n- **Extensions of Marginalized Graph Kernels (ICML 2004)**\n  - Pierre Mahe , Nobuhisa Ueda , Tatsuya Akutsu , Jean-Luc Perret , Jean-Philippe Vert\n  - [[Paper]](http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n- **Marginalized Kernels Between Labeled Graphs (ICML 2003)**\n  - Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi\n  - [[Paper]](https://pdfs.semanticscholar.org/2dfd/92c808487049ab4c9b45db77e9055b9da5a2.pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n  \n- **On Graph Kernels: Hardness Results and Efficient Alternatives (Learning Theory and Kernel Machines 2003)**\n  - Thomas Gärtner, Peter Flach, and Stefan Wrobel\n  - [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.8681\u0026rep=rep1\u0026type=pdf)\n  - [[Python Reference]](https://github.com/jajupmochi/py-graph)\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/eric-erki%2Fawesome-graph-classification/projects"}