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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

机器之心 07-20

机器之心编辑

参与:思源

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。

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 )

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 M ü ller

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 )

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 M ü ller

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 G ó mez-Bombarelli, Timothy Hirzel, Al á n 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

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 )

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

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

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 )

Tam á s Horv á th, Thomas G rtner, and Stefan Wrobel

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

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

Marginalized Kernels Between Labeled Graphs ( ICML 2003 )

Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi

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

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