Awesome Graph Embedding

A collection of graph embedding, deep learning, graph kernel and factorization papers with reference implementations.

Relevant graph classification benchmark datasets are available [here].

A similar collection on [community detection] papers with implementations.

Contents

Factorization
Spectral and Statistical Fingerprints
Deep Learning
Graph Kernels

Factorization

Learning Graph Representation via Frequent Subgraphs (SDM 2018)
- Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung
- [Paper]
- [Python Reference]
Anonymous Walk Embeddings (ICML 2018)
- Sergey Ivanov and Evgeny Burnaev
- [Paper]
- [Python Reference]
Graph2vec (MLGWorkshop 2017)
- Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
- [Paper]
- [Python High Performance]
- [Python Reference]
Subgraph2vec (MLGWorkshop 2016)
- Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
- [Paper]
- [Python High Performance]
- [Python Reference]
Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)
- Petar Ristoski and Heiko Paulheim
- [Paper]
- [Python Reference]
Deep Graph Kernels (KDD 2015)
- Pinar Yanardag and S.V.N. Vishwanathan
- [Paper]
- [Python Reference]

Spectral and Statistical Fingerprints

NetLSD (KDD 2018)
- Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller
- [Paper]
- [Python Reference]
A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)
- Nathan de Lara and Edouard Pineau
- [Paper]
- [Python Reference]
Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)
- Zixuan Zhu and Yuhai Zhao
- [Paper]
- [Python Reference]
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)
- Saurabh Verma and Zhi-Li Zhang
- [Paper]
- [Python Reference]
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]
- [Java Reference]
NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)
- Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos
- [Paper]
- [Python]

Deep Learning

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]
- [Python Reference]
Capsule Graph Neural Network (ICLR 2019)
- Zhang Xinyi and Lihui Chen
- [Paper]
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)
- Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley
- [Paper]
- [Python Reference]
Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)
- Hyeoncheol Cho and Insung. S. Choi
- [Paper]
- [Python Reference]
Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)
- Yu Jin and Joseph F. JaJa
- [Paper]
- [Python Reference]
Graph Capsule Convolutional Neural Networks (ICML 2018)
- Saurabh Verma and Zhi-Li Zhang
- [Paper]
- [Python Reference]
Graph Classification Using Structural Attention (KDD 2018)
- John Boaz Lee, Ryan Rossi, and Xiangnan Kong
- [Paper]
- [Python Pytorch Reference]
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)
- Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec
- [Paper]
- [Python Reference]
Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)
- Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec
- [Paper]
- [Python Reference]
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)
- Davide Bacciu, Federico Errica, and Alessio Micheli
- [Paper]
- [Python Reference]
MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)
- Nicola De Cao and Thomas Kipf
- [Paper]
- [Python Reference]
Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)
- Seongok Ryu, Jaechang Lim, and Woo Youn Kim
- [Paper]
- [Python Reference]
Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)
- Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes
- [Paper]
- [Python Reference]
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]
- [Python Reference]
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]
- [Python Reference]
Residual Gated Graph ConvNets (ICLR 2018)
- Xavier Bresson and Thomas Laurent
- [Paper]
- [Python Pytorch Reference]
An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)
- Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen
- [Paper]
- [Python Tensorflow Reference]
- [Python Pytorch Reference]
- [MATLAB Reference]
SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)
- Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller
- [Paper]
- [Python Reference]
Deep Learning with Topological Signatures (NIPS 2017)
- Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl
- [paper]
- [Python Reference]
Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)
- Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola
- [Paper]
- [Python Reference]
Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)
- Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur
- [Paper]
- [Python Reference]
Graph Classification with 2D Convolutional Neural Networks (2017)
- Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis
- [Paper]
- [Python Reference]
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)
- Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
- [Paper]
- [Python Reference]
Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)
- Hai Nguyen, Shin-ichi Maeda, Kenta Oono
- [Paper]
- [Python Reference]
Kernel Graph Convolutional Neural Networks (2017)
- Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
- [Paper]
- [Python Reference]
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]
- [Python Reference]
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]
- [Python Reference]

Graph Kernels

Message Passing Graph Kernels (2018)
- Giannis Nikolentzos, Michalis Vazirgiannis
- [Paper]
- [Python Reference]
Matching Node Embeddings for Graph Similarity (AAAI 2017)
- Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis
- [Paper]
Global Weisfeiler-Lehman Graph Kernels (2017)
- Christopher Morris, Kristian Kersting and Petra Mutzel
- [Paper]
- [C++ Reference]
On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)
- Nils Kriege, Pierre-Louis Giscard, Richard Wilson
- [Paper]
- [Java Reference]
Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)
- Stephen Bonner, John Brennan, and A. Stephen McGough
- [Paper]
- [python Reference]
The Multiscale Laplacian Graph Kernel (NIPS 2016)
- Risi Kondor and Horace Pan
- [Paper]
- [C++ Reference]
Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)
- Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel
- [Paper]
- [Python Reference]
Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)
- Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian
- [Paper]
- [Matlab Reference]
Halting Random Walk Kernels (NIPS 2015)
- Mahito Sugiyama and Karsten M. Borgward
- [Paper]
- [C++ Reference]
Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)
- Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
- [Paper]
Subgraph Matching Kernels for Attributed Graphs (ICML 2012)
- Nils Kriege and Petra Mutzel
- [Paper]
- [Python Reference]
Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)
- Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang
- [Paper]
- [Python Reference]
Weisfeiler-Lehman Graph Kernels (JMLR 2011)
- Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
- [Paper]
- [Python Reference]
- [C++ Reference]
Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)
- Fabrizio Costa and Kurt De Grave
- [Paper]
- [C++ Reference]
- [Python Reference]
A Linear-time Graph Kernel (ICDM 2009)
- Shohei Hido and Hisashi Kashima
- [Paper]
- [Python Reference]
Weisfeiler-Lehman Subtree Kernels (NIPS 2009)
- Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
- [Paper]
- [Python Reference]
- [C++ Reference]
Fast Computation of Graph Kernels (NIPS 2006)
- S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph
- [Paper]
- [Python Reference]
- [C++ Reference]
Shortest-Path Kernels on Graphs (ICDM 2005)
- Karsten M. Borgwardt and Hans-Peter Kriegel
- [Paper]
- [C++ Reference]
Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)
- Tamás Horváth, Thomas Gärtner, and Stefan Wrobel
- [Paper]
- [Python Reference]
Extensions of Marginalized Graph Kernels (ICML 2004)
- Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert
- [Paper]
- [Python Reference]
Marginalized Kernels Between Labeled Graphs (ICML 2003)
- Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi
- [Paper]
- [Python Reference]

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