专栏首页机器之心想要了解图或图神经网络?没有比看论文更好的方式了

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)
    • 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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原始发表时间:2019-07-20

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