【论文整理】图分类论文集合！

Graph Classification Papers

Relevant graph classification benchmark datasets are available [here].

Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.

Contents

1. Factorization
2. Spectral and Statistical Fingerprints
3. Deep Learning
4. Graph Kernels

Factorization

• Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition (Pattern Recognition 2018)

  • Anjan Dutta, Pau Riba, Josep Lladós, Alicia Fornés
  • [Paper]
  • [Matlab Reference]

• Learning Graph Representation via Frequent Subgraphs (SDM 2018)

  • Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung
  • [Paper]
  • [Python Reference]

• Anonymous Walk Embeddings (ICML 2018)

  • Sergey Ivanov and Evgeny Burnaev
  • [Paper]
  • [Python Reference]

• Graph2vec (MLGWorkshop 2017)

  • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
  • [Paper]
  • [Python High Performance]
  • [Python Reference]

• Subgraph2vec (MLGWorkshop 2016)

  • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
  • [Paper]
  • [Python High Performance]
  • [Python Reference]

• Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)

  • Petar Ristoski and Heiko Paulheim
  • [Paper]
  • [Python Reference]

• Deep Graph Kernels (KDD 2015)

  • Pinar Yanardag and S.V.N. Vishwanathan
  • [Paper]
  • [Python Reference]

Spectral and Statistical Fingerprints

• A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)

  • Chen Cai, Yusu Wang
  • [Paper]
  • [Python Reference]

• NetLSD (KDD 2018)

  • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller
  • [Paper]
  • [Python Reference]

• A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)

  • Nathan de Lara and Edouard Pineau
  • [Paper]
  • [Python Reference]

• Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)

  • Zixuan Zhu and Yuhai Zhao
  • [Paper]
  • [Python Reference]

• Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)

  • Saurabh Verma and Zhi-Li Zhang
  • [Paper]
  • [Python Reference]

• Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)

  • Shirui Pan, Jia Wu, Xingquan Zhuy, Chengqi Zhang, and Philip S. Yuz
  • [Paper]
  • [Java Reference]

• NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)

  • Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos
  • [Paper]
  • [Python]

Deep Learning

• GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)

  • Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang
  • [Paper]
  • [Python Reference]

• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (NeurIPS 2019)

  • Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu
  • [Paper]
  • [Python Reference]

• Molecule Property Prediction Based on Spatial Graph Embedding (Journal of Cheminformatics Models 2019)

  • Xiaofeng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei
  • [Paper]
  • [Python Reference]

• Fast Training of Sparse Graph Neural Networks on Dense Hardware (Arxiv 2019)

  • Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow
  • [Paper]
  • [Python Reference]

• Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)

  • Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi
  • [Paper]
  • [Python Reference]

• Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)

  • Ting Chen, Song Bian, Yizhou Sun
  • [Paper]
  • [Python Reference]

• Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification (ECML-PKDD 2019)

  • Lu Bai, Yuhang Jiao, Lixin Cui, Edwin R. Hancock
  • [Paper]
  • [Python Reference]

• Relational Pooling for Graph Representations (ICML 2019)

  • Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
  • [Paper]
  • [Python Reference]

• Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structure (ICML 2019)

  • Ruo-Chun Tzeng, Shan-Hung Wu
  • [Paper]
  • [Python Reference]

• Self-Attention Graph Pooling (ICML 2019)

  • Junhyun Lee, Inyeop Lee, Jaewoo Kang
  • [Paper]
  • [Python Reference]

• Variational Recurrent Neural Networks for Graph Classification (ICLR 2019)

  • Edouard Pineau, Nathan de Lara
  • [Paper]
  • [Python Reference]

• Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)

  • Takenori Yamamoto
  • [Paper]
  • [Python Reference]

• Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)

  • Federico Baldassarre, Hossein Azizpour
  • [Paper]
  • [Python Reference]

• Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)

  • Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang
  • [Paper]
  • [Python Reference]

• Capsule Graph Neural Network (ICLR 2019)

  • Zhang Xinyi and Lihui Chen
  • [Paper]
  • [Python Reference]

• How Powerful are Graph Neural Networks? (ICLR 2019)

  • Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
  • [Paper]
  • [Python Reference]

• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI 2019)

  • Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe
  • [Paper]
  • [Python Reference]

• Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)

  • Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley
  • [Paper]
  • [Python Reference]

• Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction (NIPS 2019)

  • Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson
  • [Paper]
  • [Python Reference]

• Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters 2018)

  • Masashi Tsubaki and Teruyasu Mizoguchi
  • [Paper]
  • [Python Reference]

• Machine Learning for Organic Cage Property Prediction (Chemical Matters 2018)

  • Lukas Turcani, Rebecca Greenway, Kim Jelfs
  • [Paper]
  • [Python Reference]

• Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)

  • Hyeoncheol Cho and Insung. S. Choi
  • [Paper]
  • [Python Reference]

• Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)

  • Yu Jin and Joseph F. JaJa
  • [Paper]
  • [Python Reference]

• Graph Capsule Convolutional Neural Networks (ICML 2018)

  • Saurabh Verma and Zhi-Li Zhang
  • [Paper]
  • [Python Reference]

• Graph Classification Using Structural Attention (KDD 2018)

  • John Boaz Lee, Ryan Rossi, and Xiangnan Kong
  • [Paper]
  • [Python Pytorch Reference]

• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)

  • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec
  • [Paper]
  • [Python Reference]

• Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)

  • Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec
  • [Paper]
  • [Python Reference]

• Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)

  • Davide Bacciu, Federico Errica, and Alessio Micheli
  • [Paper]
  • [Python Reference]

• MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)

  • Nicola De Cao and Thomas Kipf
  • [Paper]
  • [Python Reference]

• Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)

  • Seongok Ryu, Jaechang Lim, and Woo Youn Kim
  • [Paper]
  • [Python Reference]

• Compound-Protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)

  • Masashi Tsubaki, Kentaro Tomii, and Jun Sese
  • [Paper]
  • [Python Reference]
  • [Python Reference]
  • [Python Alternative ]

• Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)

  • Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes
  • [Paper]
  • [Python Reference]

• Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)

  • Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi
  • [Paper]
  • [Python Reference]

• Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)

  • Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu
  • [Paper]
  • [Python Reference]

• Residual Gated Graph ConvNets (ICLR 2018)

  • Xavier Bresson and Thomas Laurent
  • [Paper]
  • [Python Pytorch Reference]

• An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)

  • Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen
  • [Paper]
  • [Python Tensorflow Reference]
  • [Python Pytorch Reference]
  • [MATLAB Reference]
  • [Python Alternative]
  • [Python Alternative]

• SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)

  • Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller
  • [Paper]
  • [Python Reference]

• Deep Learning with Topological Signatures (NIPS 2017)

  • Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl
  • [paper]
  • [Python Reference]

• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)

  • Martin Simonovsky and Nikos Komodakis
  • [paper]
  • [Python Reference]

• Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)

  • Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola
  • [Paper]
  • [Python Reference]

• Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)

  • Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur
  • [Paper]
  • [Python Reference]

• Graph Classification with 2D Convolutional Neural Networks (2017)

  • Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis
  • [Paper]
  • [Python Reference]

• CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)

  • Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
  • [Paper]
  • [Python Reference]

• Semi-Supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)

  • Hai Nguyen, Shin-ichi Maeda, Kenta Oono
  • [Paper]
  • [Python Reference]

• Kernel Graph Convolutional Neural Networks (2017)

  • Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
  • [Paper]
  • [Python Reference]

• Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)

  • Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough
  • [Paper]
  • [Python Reference]

• Learning Convolutional Neural Networks for Graphs (ICML 2016)

  • Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
  • [Paper]
  • [Python Reference]

• Gated Graph Sequence Neural Networks (ICLR 2016)

  • Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
  • [Paper]
  • [Python TensorFlow]
  • [Python PyTorch]
  • [Python Reference]

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)

  • David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams
  • [Paper]
  • [Python Reference]
  • [Python Reference]
  • [Python Reference]
  • [Python Reference]

Graph Kernels

• A Persistent Weisfeiler–Lehman Procedure for Graph Classification (ICML 2019)

  • Sebastian Rieck, Christian Bock, and Karsten Borgwardt
  • [Paper]
  • [Python Reference]

• Message Passing Graph Kernels (2018)

  • Giannis Nikolentzos, Michalis Vazirgiannis
  • [Paper]
  • [Python Reference]

• Matching Node Embeddings for Graph Similarity (AAAI 2017)

  • Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis
  • [Paper]

• Global Weisfeiler-Lehman Graph Kernels (2017)

  • Christopher Morris, Kristian Kersting and Petra Mutzel
  • [Paper]
  • [C++ Reference]

• On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)

  • Nils Kriege, Pierre-Louis Giscard, Richard Wilson
  • [Paper]
  • [Java Reference]

• Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)

  • Stephen Bonner, John Brennan, and A. Stephen McGough
  • [Paper]
  • [python Reference]

• The Multiscale Laplacian Graph Kernel (NIPS 2016)

  • Risi Kondor and Horace Pan
  • [Paper]
  • [C++ Reference]

• Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)

  • Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel
  • [Paper]
  • [Python Reference]

• Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)

  • Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian
  • [Paper]
  • [Matlab Reference]

• Halting Random Walk Kernels (NIPS 2015)

  • Mahito Sugiyama and Karsten M. Borgward
  • [Paper]
  • [C++ Reference]

• A Graph Kernel Based on the Jensen-Shannon Representation Alignment (IJCAI 2015)

  • Lu Bai, Zhihong Zhang, Chaoyan Wang, Xiao Bai, Edwin R. Hancock
  • [Paper]
  • [Matlab reference]

• An Aligned Subtree Kernel for Weighted Graphs (ICML 2015)

  • Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock
  • [Paper]

• Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)

  • Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
  • [Paper]

• Subgraph Matching Kernels for Attributed Graphs (ICML 2012)

  • Nils Kriege and Petra Mutzel
  • [Paper]
  • [Python Reference]

• Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)

  • Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang
  • [Paper]
  • [Python Reference]

• Weisfeiler-Lehman Graph Kernels (JMLR 2011)

  • Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
  • [Paper]
  • [Python Reference]
  • [Python Reference]
  • [C++ Reference]

• Two New Graphs Kernels in Chemoinformatics (Pattern Recognition Letters 2012)

  • Benoit Gaüzère, LucBrun, and Didier Villemin
  • [Paper]
  • [Python Reference]

• Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

  • Fabrizio Costa and Kurt De Grave
  • [Paper]
  • [C++ Reference]
  • [Python Reference]

• Graph Kernels (JMLR 2010)

  • S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt;
  • [Paper]
  • [Python Reference]

• A Linear-time Graph Kernel (ICDM 2009)

  • Shohei Hido and Hisashi Kashima
  • [Paper]
  • [Python Reference]

• Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

  • Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
  • [Paper]
  • [Python Reference]
  • [Python Reference]
  • [C++ Reference]

• Kernel on Bag of Paths For Measuring Similarity of Shapes (InESANN 2007)

  • Frederic Suard, Alain Rakotomamonjy, and Abdelaziz Bensrhair
  • [Paper]
  • [Python Reference]

• Fast Computation of Graph Kernels (NIPS 2006)

  • S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph
  • [Paper]
  • [Python Reference]
  • [C++ Reference]

• Shortest-Path Kernels on Graphs (ICDM 2005)

  • Karsten M. Borgwardt and Hans-Peter Kriegel
  • [Paper]
  • [C++ Reference]

• Graph Kernels for Chemical Informatics (Neural Networks 2005)

  • Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi
  • [Paper]
  • [Python Reference]

• Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)

  • Tamás Horváth, Thomas Gärtner, and Stefan Wrobel
  • [Paper]
  • [Python Reference]

• Extensions of Marginalized Graph Kernels (ICML 2004)

  • Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert
  • [Paper]
  • [Python Reference]

• Extensions of Marginalized Graph Kernels (ICML 2004)

  • Pierre Mahe , Nobuhisa Ueda , Tatsuya Akutsu , Jean-Luc Perret , Jean-Philippe Vert
  • [Paper]
  • [Python Reference]

• Marginalized Kernels Between Labeled Graphs (ICML 2003)

  • Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi
  • [Paper]
  • [Python Reference]

• On Graph Kernels: Hardness Results and Efficient Alternatives (Learning Theory and Kernel Machines 2003)

  • Thomas Gärtner, Peter Flach, and Stefan Wrobel
  • [Paper]
  • [Python Reference]