送你200+篇论文，学习图或图神经网络必读！（附下载）

来源：机器之心

本文约2300字，建议阅读5分钟。

这篇文章将介绍你需要的图建模论文，当然它们都有配套实现的。

图是一种非常神奇的表示方式，生活中绝大多数的现象或情境都能用图来表示，例如人际关系网、道路交通网、信息互联网等等。正如马哲介绍事物具有普遍联系性，而图正好能捕捉这种联系，所以用它来描述这个世界是再好不过的方法。

但图这种结构化数据有个麻烦的地方，我们先要有图才能进行后续的计算。但图的搭建并不简单，目前也没有比较好的自动化方法，所以第一步还是需要挺多功夫的。只要各节点及边都确定了，那么图就是一种非常强大且复杂的工具，模型也能推断出图中的各种隐藏知识。

不同时期的图建模

其实，我们可以将图建模分为图神经网络与传统的图模型。其中以前的图建模主要借助 Graph Embedding 为不同的节点学习低维向量表征，这借鉴了 NLP 中词嵌入的思想。而图神经网络借助深度学习进行更强大的图运算与图表征。

Graph Embedding 算法聚焦在如何对网络节点进行低维向量表示，相似的节点在表征空间中更加接近。相比之下，GNN 最大的优势在于它不只可以对一个节点进行语义表示。

例如 GNN 可以表示子图的语义信息，将网络中一小部分节点构成的语义表示出来，这是以前 Graph Embedding 不容易做到的。GNN 还可以在整个图网络上进行信息传播、聚合等建模，也就是说它可以把图网络当成一个整体进行建模。此外，GNN 对单个节点的表示也可以做得更好，因为它可以更好地建模周围节点丰富信息。

在传统图建模中，随机游走、最短路径等图方法会利用符号知识，但这些方法并没有办法很好地利用每个节点的语义信息。而深度学习技术更擅长处理非结构文本、图像等数据。简言之，我们可以将 GNN 看做将深度学习技术应用到符号表示的图数据上，或者说是从非结构化数据扩展到了结构化数据。GNN 能够充分融合符号表示和低维向量表示，发挥两者优势。

图建模论文与代码

在 GitHub 的一项开源工作中，开发者收集了图建模相关的论文与实现，并且从经典的 Graph Embedding、Graph Kernel 到图神经网络都有涉及。它们在图嵌入、图分类、图表征等领域都是非常重要的论文。

项目地址：

https://github.com/benedekrozemberczki/awesome-graph-classification

该项目主要收集的论文领域如下所示：

1. Factorization

2. Spectral and Statistical Fingerprints

3. Graph Neural Network

4. Graph Kernels

   
   

因式分解法

• Learning Graph Representation via Frequent Subgraphs (SDM 2018)
  

• Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung

• Paper：

  https://epubs.siam.org/doi/10.1137/1.9781611975321.35

• Python：

  https://github.com/nphdang/GE-FSG

• Anonymous Walk Embeddings (ICML 2018)

• Sergey Ivanov and Evgeny Burnaev

• Paper：

  https://arxiv.org/pdf/1805.11921.pdf

• Python：

  https://github.com/nd7141/AWE

• Graph2vec (MLGWorkshop 2017)

• Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan

• Paper：

  https://arxiv.org/abs/1707.05005

• Python High Performance：

  https://github.com/benedekrozemberczki/graph2vec

• Python Reference：

  https://github.com/MLDroid/graph2vec_tf

• Subgraph2vec (MLGWorkshop 2016)

• Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan

• Paper：

  https://arxiv.org/abs/1606.08928

• Python High Performance：

  https://github.com/MLDroid/subgraph2vec_gensim

• Python Reference：

  https://github.com/MLDroid/subgraph2vec_tf

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

• Petar Ristoski and Heiko Paulheim

• Paper：

  https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30

• Python Reference：

  https://github.com/airobert/RDF2VecAtWebScale

• Deep Graph Kernels (KDD 2015)

• Pinar Yanardag and S.V.N. Vishwanathan

• Paper：

  https://dl.acm.org/citation.cfm?id=2783417

• Python Reference：

  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

• Paper：

  https://arxiv.org/abs/1811.03508

• Python Reference：

  https://github.com/Chen-Cai-OSU/LDP

• NetLSD (KDD 2018)

• Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller

• Paper：

  https://arxiv.org/abs/1805.10712

• Python Reference：

  https://github.com/xgfs/NetLSD

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

• Nathan de Lara and Edouard Pineau

• Paper：

  https://arxiv.org/pdf/1810.09155.pdf

• Python Reference：

  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

• Paper：

  https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf

• Python Reference：

  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

• Paper：

  https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf

• Python Reference：

  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

• Paper：

  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

• Paper：

  https://arxiv.org/abs/1209.2684

• Python：

  https://github.com/kristyspatel/Netsimile

  
  

图神经网络

• Self-Attention Graph Pooling (ICML 2019)

• Junhyun Lee, Inyeop Lee, Jaewoo Kang

• Paper：

  https://arxiv.org/abs/1904.08082

• Python Reference：

  https://github.com/inyeoplee77/SAGPool

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

• Edouard Pineau, Nathan de Lara

• Paper：

  https://arxiv.org/abs/1902.02721

• Python Reference：

  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

• Paper：

  https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf

• Python Reference：

  https://github.com/Tony-Y/cgnn

• Explainability Techniques for Graph Convolutional Networks (ICML 2019)

• Federico Baldassarre, Hossein Azizpour

• Paper：

  https://128.84.21.199/pdf/1905.13686.pdf

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1904.05003.pdf

• Python Reference：

  https://github.com/benedekrozemberczki/SEAL-CI

• Capsule Graph Neural Network (ICLR 2019)

• Zhang Xinyi and Lihui Chen

• Paper：

  https://openreview.net/forum?id=Byl8BnRcYm

• Python Reference：

  https://github.com/benedekrozemberczki/CapsGNN

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

• Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka

• Paper：

  https://arxiv.org/abs/1810.00826

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1810.02244v2.pdf

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1902.08399v1.pdf

• Python Reference：

  https://github.com/BraintreeLtd/PatchyCapsules

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

• Hyeoncheol Cho and Insung. S. Choi

• Paper：

  https://arxiv.org/abs/1811.09794

• Python Reference：

  https://github.com/blackmints/3DGCN

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

• Yu Jin and Joseph F. JaJa

• Paper：

  https://arxiv.org/pdf/1805.07683v4.pdf

• Python Reference：

  https://github.com/yuj-umd/graphRNN

• Graph Capsule Convolutional Neural Networks (ICML 2018)

• Saurabh Verma and Zhi-Li Zhang

• Paper：

  https://arxiv.org/abs/1805.08090

• Python Reference：

  https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks

• Graph Classification Using Structural Attention (KDD 2018)

• John Boaz Lee, Ryan Rossi, and Xiangnan Kong

• Paper：

  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

• Paper：

  https://arxiv.org/abs/1806.02473

• Python Reference：

  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

• Paper：

  http://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1805.10636.pdf

• Python Reference：

  https://github.com/diningphil/CGMM

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

• Nicola De Cao and Thomas Kipf

• Paper：

  https://arxiv.org/pdf/1805.11973.pdf

• Python Reference：

  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

• Paper：

  https://arxiv.org/abs/1805.10988

• Python Reference：

  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

• Paper：

  https://academic.oup.com/bioinformatics/article/35/2/309/5050020

• Python Reference：

  https://github.com/masashitsubaki/CPI_prediction

• Python Reference：

  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

• Paper：

  https://ieeexplore.ieee.org/abstract/document/8545310

• Python Reference：

  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

• Paper：

  https://arxiv.org/abs/1802.04944v1

• Python Reference：

  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

• Paper：

  http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf

• Python Reference：

  https://github.com/tuxchow/ccm

• Residual Gated Graph ConvNets (ICLR 2018)

• Xavier Bresson and Thomas Laurent

• Paper：

  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

• Paper：

  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

• Paper：

  https://arxiv.org/abs/1807.02839

• Python Reference：

  http://mott.in/publications/others/sgr/

• Deep Learning with Topological Signatures (NIPS 2017)

• Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl

• paper：

  https://arxiv.org/abs/1707.04041

• Python Reference：

  https://github.com/c-hofer/nips2017

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

• Martin Simonovsky and Nikos Komodakis

• paper：

  https://arxiv.org/pdf/1704.02901v3.pdf

• Python Reference：

  https://github.com/mys007/ecc

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

• Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola

• Paper：

  https://arxiv.org/abs/1705.09037

• Python Reference：

  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

• Paper：

  https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks

• Python Reference：

  https://github.com/fouticus/pipgcn

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

• Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis

• Paper：

  https://arxiv.org/abs/1708.02218

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1705.07664v2.pdf

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1711.10168.pdf

• Python Reference：

  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

• Paper：

  https://arxiv.org/pdf/1710.10689.pdf

• Python Reference：

  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

• Paper：

  https://ieeexplore.ieee.org/document/7840988/

• Python Reference：

  https://github.com/sbonner0/DeepTopologyClassification

• Learning Convolutional Neural Networks for Graphs (ICML 2016)

• Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov

• Paper：

  https://arxiv.org/abs/1605.05273

• Python Reference：

  https://github.com/tvayer/PSCN

• Gated Graph Sequence Neural Networks (ICLR 2016)

• Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel

• Paper：

  https://arxiv.org/abs/1511.05493

• Python TensorFlow：

  https://github.com/bdqnghi/ggnn.tensorflow

• Python PyTorch：

  https://github.com/JamesChuanggg/ggnn.pytorch

• Python Reference：

  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

• Paper：

  https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf

• Python Reference：

  https://github.com/fllinares/neural_fingerprints_tf

• Python Reference：

  https://github.com/jacklin18/neural-fingerprint-in-GNN

• Python Reference：

  https://github.com/HIPS/neural-fingerprint

• Python Reference：

  https://github.com/debbiemarkslab/neural-fingerprint-theano

Graph Kernels

• Message Passing Graph Kernels (2018)

• Giannis Nikolentzos, Michalis Vazirgiannis

• Paper：

  https://arxiv.org/pdf/1808.02510.pdf

• Python Reference：

  https://github.com/giannisnik/message_passing_graph_kernels

• Matching Node Embeddings for Graph Similarity (AAAI 2017)

• Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis

• Paper：

  https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494

• Global Weisfeiler-Lehman Graph Kernels (2017)

• Christopher Morris, Kristian Kersting and Petra Mutzel

• Paper：

  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

• Paper：

  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

• Paper：

  http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt

• python Reference：

  https://github.com/sbonner0/GraphFingerprintComparison

• The Multiscale Laplacian Graph Kernel (NIPS 2016)

• Risi Kondor and Horace Pan

• Paper：

  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

• Paper：

  https://arxiv.org/abs/1610.00064

• Python Reference：

  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

• Paper：

  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

• Paper：

  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

• Paper：

  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

• Paper：

  https://arxiv.org/abs/1206.6483

• Python Reference：

  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

• Paper：

  https://ieeexplore.ieee.org/document/6413884/

• Python Reference：

  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

• Paper：

  http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf

• Python Reference：

  https://github.com/jajupmochi/py-graph

• Python Reference：

  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

• Paper：

  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

• Python Reference：

  https://github.com/fabriziocosta/EDeN

• A Linear-time Graph Kernel (ICDM 2009)

• Shohei Hido and Hisashi Kashima

• Paper：

  https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243

• Python Reference：

  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

• Paper：

  http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf

• Python Reference：

  https://github.com/jajupmochi/py-graph

• Python Reference：

  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

• Paper：

  http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf

• Python Reference：

  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

• Paper：

  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

• Paper：

  http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158&rep=rep1&type=pdf

• Python Reference：

  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

• Paper：

  http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf

• Python Reference：

  https://github.com/jajupmochi/py-graph

• Marginalized Kernels Between Labeled Graphs (ICML 2003)

• Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi

• Paper：

  https://pdfs.semanticscholar.org/2dfd/92c808487049ab4c9b45db77e9055b9da5a2.pdf

• Python Reference：

  https://github.com/jajupmochi/py-graph

  
  

  编辑：于腾凯

  校对：林亦霖