To graph or map the neural network? There is no better way than reading papers

Figure embedded, drawing characterization, classification map, map neural network, this article will introduce the graph modeling papers you need, of course, they are supporting the implementation.

Figure is a fantastic representation of life in the vast majority of the phenomenon or situation can be represented by drawing, such as personal relationships, and road network, the Internet information and so on. As Margery introduction of universal connectedness of things, but just to capture the view of such a link, so use it to describe the world is no better way.

But this view of structured data has a troublesome place, we need to have a map for subsequent calculations. But the figure is not easy to build, and there are no better automated method, so the first step still needs a lot of effort. As long as each node and edge are identified, then the map is a very powerful and complex tools, the model can infer various hidden knowledge graph.

 

 

FIG modeling different periods

In fact, we can map the neural network modeling into drawing with traditional graph model. Modeling for which previous major means of Graph Embedding low dimensional vectors characterize learning for different nodes, which draws on NLP word embedded in the idea. FIG depth while the neural network by means of learning a more powerful computing and FIG Characterization of FIG.

Graph Embedding Algorithm focus on how the network nodes represent low dimensional vectors, similar nodes in closer characterization space. In contrast, GNN's greatest strength is that it can be not only a semantic representation of a node.

GNN semantic information may indicate, for example, sub-picture, the semantic network nodes constituting a small portion of that out, which was previously Graph Embedding not easy to do. GNN information can also be spread over the entire polymerization diagram of a network model, that is to say it can diagram of a network model as a whole. In addition, GNN represents a single node can do better, because it can be better modeled around a wealth of information nodes.

In FIG conventional modeling, random walk, the shortest path method of FIG other symbol would use the knowledge, but these methods are not good way to use semantic information for each node. And learning techniques was more at a depth of unstructured text data, images and the like. In short, we can be seen on the map GNN depth data will learn techniques to notation, or is extended from unstructured data to structured data. GNN notation can be fully integrated and low-dimensional vector representation, to play the advantages of both.

Figure Modeling Papers and Codes

 

In a work of GitHub open source, developers collect a paper graph modeling and implementation related, and from classic Graph Embedding, Graph Kernel to map the neural network are involved. They are very important paper in the embedded chart, map classified, map characterizes other fields.

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

 

The project area of ​​paper collected as follows:

1. Factorization

2. Spectral and Statistical Fingerprints

3. Graph Neural Network

4. Graph Kernels

Factorization method

• 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)
• Peter 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 SVN 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 Lara and Edward 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

Figure neural network

• 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)
• Pineau Edward Nathan 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)
• David Baccio, 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, Joseph Lladós 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, alan 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 wars, 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. wars, 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 wars 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)
• Tamas Horvath, Thomas Gärtner, Stefan Wrobel and
• 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

 

FIG modeling different periods

In fact, we can map the neural network modeling into drawing with traditional graph model. Modeling for which previous major means of Graph Embedding low dimensional vectors characterize learning for different nodes, which draws on NLP word embedded in the idea. FIG depth while the neural network by means of learning a more powerful computing and FIG Characterization of FIG.

Graph Embedding Algorithm focus on how the network nodes represent low dimensional vectors, similar nodes in closer characterization space. In contrast, GNN's greatest strength is that it can be not only a semantic representation of a node.

GNN semantic information may indicate, for example, sub-picture, the semantic network nodes constituting a small portion of that out, which was previously Graph Embedding not easy to do. GNN information can also be spread over the entire polymerization diagram of a network model, that is to say it can diagram of a network model as a whole. In addition, GNN represents a single node can do better, because it can be better modeled around a wealth of information nodes.

In FIG conventional modeling, random walk, the shortest path method of FIG other symbol would use the knowledge, but these methods are not good way to use semantic information for each node. And learning techniques was more at a depth of unstructured text data, images and the like. In short, we can be seen on the map GNN depth data will learn techniques to notation, or is extended from unstructured data to structured data. GNN notation can be fully integrated and low-dimensional vector representation, to play the advantages of both.

Figure Modeling Papers and Codes

In an open-source work, developers collect a paper graph modeling and implementation related, and from classic Graph Embedding, Graph Kernel to map the neural network are involved. They are very important paper in the embedded chart, map classified, map characterizes other fields.

Project Address: benedekrozemberczki / awesome-graph-classification

The project area of ​​paper collected as follows:

1. Factorization

2. Spectral and Statistical Fingerprints

3. Graph Neural Network

4. Graph Kernels

Factorization method

· 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：nphdang/GE-FSG

· Anonymous Walk Embeddings (ICML 2018)

· Sergey Ivanov and Evgeny Burnaev

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

· Python：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：benedekrozemberczki/graph2vec

· Python Reference：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：MLDroid/subgraph2vec_gensim

· Python Reference：MLDroid/subgraph2vec_tf

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

· Peter Ristoski and Heiko Paulheim

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

· Python Reference：airobert/RDF2VecAtWebScale

· Deep Graph Kernels (KDD 2015)

· Pinar Yanardag and SVN Vishwanathan

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

· Python Reference：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：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：xgfs/NetLSD

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

· Nathan Lara and Edward Pineau

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

· Python Reference：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:// .com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf

· Python Reference：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:// .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:// .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: // .com / kristyspatel / Netsimile