图分类相关资源大列表

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图分类相关资源大列表

2019 年 7 月 18 日 专知

【导读】本文整理了图分类方法的集合，包括嵌入，深度学习，图形内核和分解论文以及参考实现。

Factorization

Learning Graph Representation via Frequent Subgraphs (SDM 2018)

Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung
https://epubs.siam.org/doi/10.1137/1.9781611975321.35
https://github.com/nphdang/GE-FSG

Anonymous Walk Embeddings (ICML 2018)

Sergey Ivanov and Evgeny Burnaev
https://arxiv.org/pdf/1805.11921.pdf
https://github.com/nd7141/AWE

Graph2vec (MLGWorkshop 2017)

Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
https://arxiv.org/abs/1707.05005
[[Python High Performance]]https://github.com/benedekrozemberczki/graph2vec
https://github.com/MLDroid/graph2vec_tf

Subgraph2vec (MLGWorkshop 2016)

Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
https://arxiv.org/abs/1606.08928
[[Python High Performance]]https://github.com/MLDroid/subgraph2vec_gensim
https://github.com/MLDroid/subgraph2vec_tf

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

Petar Ristoski and Heiko Paulheim
https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30
https://github.com/airobert/RDF2VecAtWebScale

Deep Graph Kernels (KDD 2015)

Pinar Yanardag and S.V.N. Vishwanathan
https://dl.acm.org/citation.cfm?id=2783417
https://github.com/pankajk/Deep-Graph-Kernels

Spectral and Statistical Fingerprints

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

Chen Cai, Yusu Wang
https://arxiv.org/abs/1811.03508
https://github.com/Chen-Cai-OSU/LDP

NetLSD (KDD 2018)

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller
https://arxiv.org/abs/1805.10712
https://github.com/xgfs/NetLSD

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

Nathan de Lara and Edouard Pineau
https://arxiv.org/pdf/1810.09155.pdf
https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification

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

Zixuan Zhu and Yuhai Zhao
https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf
https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning

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

Saurabh Verma and Zhi-Li Zhang
https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf
https://github.com/vermaMachineLearning/FGSD

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
https://ieeexplore.ieee.org/document/7302040
[[Java Reference]]https://github.com/shiruipan/MTG

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

Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos
https://arxiv.org/abs/1209.2684
[[Python]]https://github.com/kristyspatel/Netsimile

Deep Learning

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

Ruo-Chun Tzeng, Shan-Hung Wu
http://proceedings.mlr.press/v97/tzeng19a/tzeng19a.pdf
https://github.com/rutzeng/EgoCNN

Self-Attention Graph Pooling (ICML 2019)

Junhyun Lee, Inyeop Lee, Jaewoo Kang
https://arxiv.org/abs/1904.08082
https://github.com/inyeoplee77/SAGPool

Variational Recurrent Neural Networks for Graph Classification (ICLR 2019)

Edouard Pineau, Nathan de Lara
https://arxiv.org/abs/1902.02721
https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification

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

Takenori Yamamoto
https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf
https://github.com/Tony-Y/cgnn

Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)

Federico Baldassarre, Hossein Azizpour
https://128.84.21.199/pdf/1905.13686.pdf
https://github.com/gn-exp/gn-exp

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

Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang
https://arxiv.org/pdf/1904.05003.pdf
https://github.com/benedekrozemberczki/SEAL-CI

Capsule Graph Neural Network (ICLR 2019)

Zhang Xinyi and Lihui Chen
https://openreview.net/forum?id=Byl8BnRcYm
https://github.com/benedekrozemberczki/CapsGNN

How Powerful are Graph Neural Networks? (ICLR 2019)

Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
https://arxiv.org/abs/1810.00826
https://github.com/weihua916/powerful-gnns

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
https://arxiv.org/pdf/1810.02244v2.pdf
https://github.com/k-gnn/k-gnn

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

Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley
https://arxiv.org/pdf/1902.08399v1.pdf
https://github.com/BraintreeLtd/PatchyCapsules

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

Hyeoncheol Cho and Insung. S. Choi
https://arxiv.org/abs/1811.09794
https://github.com/blackmints/3DGCN

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

Yu Jin and Joseph F. JaJa
https://arxiv.org/pdf/1805.07683v4.pdf
https://github.com/yuj-umd/graphRNN

Graph Capsule Convolutional Neural Networks (ICML 2018)

Saurabh Verma and Zhi-Li Zhang
https://arxiv.org/abs/1805.08090
https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks

Graph Classification Using Structural Attention (KDD 2018)

John Boaz Lee, Ryan Rossi, and Xiangnan Kong
http://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf
[[Python Pytorch Reference]]https://github.com/benedekrozemberczki/GAM

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

Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec
https://arxiv.org/abs/1806.02473
https://github.com/bowenliu16/rl_graph_generation

Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)

Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec
http://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf
https://github.com/rusty1s/pytorch_geometric

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

Davide Bacciu, Federico Errica, and Alessio Micheli
https://arxiv.org/pdf/1805.10636.pdf
https://github.com/diningphil/CGMM

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

Nicola De Cao and Thomas Kipf
https://arxiv.org/pdf/1805.11973.pdf
https://github.com/nicola-decao/MolGAN

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

Seongok Ryu, Jaechang Lim, and Woo Youn Kim
https://arxiv.org/abs/1805.10988
https://github.com/SeongokRyu/Molecular-GAT

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
https://academic.oup.com/bioinformatics/article/35/2/309/5050020
https://github.com/masashitsubaki/CPI_prediction
https://github.com/masashitsubaki/GNN_molecules
[[Python Alternative ]]https://github.com/xnuohz/GCNDTI

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

Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes
https://ieeexplore.ieee.org/abstract/document/8545310
https://github.com/priba/siamese_ged

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
https://arxiv.org/abs/1802.04944v1
https://github.com/Luckick/EAGCN

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

Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu
http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf
https://github.com/tuxchow/ccm

Residual Gated Graph ConvNets (ICLR 2018)

Xavier Bresson and Thomas Laurent
https://arxiv.org/pdf/1711.07553v2.pdf
[[Python Pytorch Reference]]https://github.com/xbresson/spatial_graph_convnets

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

Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen
https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf
[[Python Tensorflow Reference]]https://github.com/muhanzhang/DGCNN
[[Python Pytorch Reference]]https://github.com/muhanzhang/pytorch_DGCNN
[[MATLAB Reference]]https://github.com/muhanzhang/DGCNN
[[Python Alternative]]https://github.com/leftthomas/DGCNN
[[Python Alternative]]https://github.com/hitlic/DGCNN-tensorflow

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

Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller
https://arxiv.org/abs/1807.02839
http://mott.in/publications/others/sgr/

Deep Learning with Topological Signatures (NIPS 2017)

Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl
https://arxiv.org/abs/1707.04041
https://github.com/c-hofer/nips2017

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

Martin Simonovsky and Nikos Komodakis
https://arxiv.org/pdf/1704.02901v3.pdf
https://github.com/mys007/ecc

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

Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola
https://arxiv.org/abs/1705.09037
https://github.com/taolei87/icml17_knn

Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)

Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur
https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks
https://github.com/fouticus/pipgcn

Graph Classification with 2D Convolutional Neural Networks (2017)

Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis
https://arxiv.org/abs/1708.02218
https://github.com/Tixierae/graph_2D_CNN

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

Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
https://arxiv.org/pdf/1705.07664v2.pdf
https://github.com/fmonti/CayleyNet

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

Hai Nguyen, Shin-ichi Maeda, Kenta Oono
https://arxiv.org/pdf/1711.10168.pdf
https://github.com/pfnet-research/hierarchical-molecular-learning

Kernel Graph Convolutional Neural Networks (2017)

Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
https://arxiv.org/pdf/1710.10689.pdf
https://github.com/giannisnik/cnn-graph-classification

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
https://ieeexplore.ieee.org/document/7840988/
https://github.com/sbonner0/DeepTopologyClassification

Learning Convolutional Neural Networks for Graphs (ICML 2016)

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
https://arxiv.org/abs/1605.05273
https://github.com/tvayer/PSCN

Gated Graph Sequence Neural Networks (ICLR 2016)

Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
https://arxiv.org/abs/1511.05493
[[Python TensorFlow]]https://github.com/bdqnghi/ggnn.tensorflow
[[Python PyTorch]]https://github.com/JamesChuanggg/ggnn.pytorch
https://github.com/YunjaeChoi/ggnnmols

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
https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf
https://github.com/fllinares/neural_fingerprints_tf
https://github.com/jacklin18/neural-fingerprint-in-GNN
https://github.com/HIPS/neural-fingerprint
https://github.com/debbiemarkslab/neural-fingerprint-theano

Graph Kernels

Message Passing Graph Kernels (2018)

Giannis Nikolentzos, Michalis Vazirgiannis
https://arxiv.org/pdf/1808.02510.pdf
https://github.com/giannisnik/message_passing_graph_kernels

Matching Node Embeddings for Graph Similarity (AAAI 2017)

Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis
https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494

Global Weisfeiler-Lehman Graph Kernels (2017)

Christopher Morris, Kristian Kersting and Petra Mutzel
https://arxiv.org/pdf/1703.02379.pdf
[[C++ Reference]]https://github.com/chrsmrrs/glocalwl

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

Nils Kriege, Pierre-Louis Giscard, Richard Wilson
https://arxiv.org/pdf/1606.01141.pdf
[[Java Reference]]https://github.com/nlskrg/optimal_assignment_kernels

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

Stephen Bonner, John Brennan, and A. Stephen McGough
http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt
https://github.com/sbonner0/GraphFingerprintComparison

The Multiscale Laplacian Graph Kernel (NIPS 2016)

Risi Kondor and Horace Pan
https://arxiv.org/abs/1603.06186
[[C++ Reference]]https://github.com/horacepan/MLGkernel

Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)

Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel
https://arxiv.org/abs/1610.00064
https://github.com/chrsmrrs/hashgraphkernel

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

Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian
https://link.springer.com/article/10.1007/s10994-015-5517-9
[[Matlab Reference]]https://github.com/marionmari/propagation_kernels

Halting Random Walk Kernels (NIPS 2015)

Mahito Sugiyama and Karsten M. Borgward
https://pdfs.semanticscholar.org/79ba/8bcfbf9496834fdc22a1f7c96d26d776cd6c.pdf
[[C++ Reference]]https://github.com/BorgwardtLab/graph-kernels

Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)

Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf

Subgraph Matching Kernels for Attributed Graphs (ICML 2012)

Nils Kriege and Petra Mutzel
https://arxiv.org/abs/1206.6483
https://github.com/mockingbird2/GraphKernelBenchmark

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

Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang
https://ieeexplore.ieee.org/document/6413884/
https://github.com/benedekrozemberczki/NestedSubtreeHash

Weisfeiler-Lehman Graph Kernels (JMLR 2011)

Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
https://github.com/jajupmochi/py-graph
https://github.com/deeplego/wl-graph-kernels
[[C++ Reference]]https://github.com/BorgwardtLab/graph-kernels

Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

Fabrizio Costa and Kurt De Grave
https://icml.cc/Conferences/2010/papers/347.pdf
[[C++ Reference]]https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/www.bioinf.uni-freiburg.de/~costa/EDeNcpp.tgz
https://github.com/fabriziocosta/EDeN

A Linear-time Graph Kernel (ICDM 2009)

Shohei Hido and Hisashi Kashima
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243
https://github.com/hgascon/adagio

Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf
https://github.com/jajupmochi/py-graph
https://github.com/deeplego/wl-graph-kernels
[[C++ Reference]]https://github.com/BorgwardtLab/graph-kernels

Fast Computation of Graph Kernels (NIPS 2006)

S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph
http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf
https://github.com/jajupmochi/py-graph
[[C++ Reference]]https://github.com/BorgwardtLab/graph-kernels

Shortest-Path Kernels on Graphs (ICDM 2005)

Karsten M. Borgwardt and Hans-Peter Kriegel
https://www.ethz.ch/content/dam/ethz/special-interest/bsse/borgwardt-lab/documents/papers/BorKri05.pdf
[[C++ Reference]]https://github.com/KitwareMedical/ITKTubeTK

Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)

Tamás Horváth, Thomas Gärtner, and Stefan Wrobel
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158&rep=rep1&type=pdf
https://github.com/jajupmochi/py-graph

Extensions of Marginalized Graph Kernels (ICML 2004)

Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert
http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf
https://github.com/jajupmochi/py-graph