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想要了解图或图神经网络？没有比看论文更好的方式了

 5 years ago
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图嵌入、图表征、图分类、图神经网络，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。

NFbIrea.jpg!web

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


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