Awesome Graph Embedding

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

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

Table of Contents

1. Factorization
2. Spectral and Statistical Fingerprints
3. Deep Learning
4. Graph Kernels

Paper References with the implementation(s)

Factorization

• Anonymous Walk Embeddings (ICML 2018)

  • Sergey Ivanov, Evgeny Burnaev
  • [Paper]
  • [Python Reference]

• Graph2vec (MLGWorkshop 2017)

  • Narayanan, Annamalai and Chandramohan, Mahinthan and Chen, Lihui and Liu, Yang and Saminathan, Santhoshkumar
  • [Paper]
  • [Python High Performance]
  • [Python Reference]

• Subgraph2vec (MLGWorkshop 2016)

  • Narayanan, Annamalai and Chandramohan, Mahinthan and Chen, Lihui and Liu, Yang and Saminathan, Santhoshkumar
  • [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)

  • Yanardag, Pinar and Vishwanathan, SVN
  • [Paper]
  • [Python Reference]

Spectral and Statistical Fingerprints

• NetLSD (KDD 2018)

  • Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Bronstein, Alex and Muller, Emmanuel
  • [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 Zhu, Chengqi Zhang, and Philip Yu
  • [Paper]
  • [Java Reference]

• NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)

  • Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos
  • [Paper]
  • [Python]

Deep Learning

• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)

  • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, 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, 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, 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, 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, Xiaoyan Zhu
  • [Paper]
  • [Python Reference]

• An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)

  • Zhang, Muhan and Cui, Zhicheng and Neumann, Marion and Chen, Yixin
  • [Paper]
  • [Python Tensorflow Reference]
  • [Python Pytorch Reference]

• Graph Classification Using Structural Attention (KDD 2018)

  • Lee, John Boaz and Rossi, Ryan and Kong, Xiangnan
  • [Paper]

• SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)

  • Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein, Emmanueal Müller
  • [Paper]
  • [Python Reference]

• Deep Learning with Topological Signatures (NIPS 2017)

  • DHofer, Christoph and Kwitt, Roland and Niethammer, Marc and Uhl, Andreas
  • [paper]
  • [Python Reference]

• Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)

  • Tao Lei, Wengong Jin, Regina Barzilay, 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)

  • Tixier, Antoine J-P and Nikolentzos, Giannis and Meladianos, Polykarpos and Vazirgiannis, Michalis
  • [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)

  • Nikolentzos, Giannis and Meladianos, Polykarpos and Tixier, Antoine Jean-Pierre and Skianis, Konstantinos and Vazirgiannis, Michalis
  • [Paper]
  • [Python Reference]

• Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)

  • Bonner, Stephen and Brennan, John and Theodoropoulos, Georgios and McGough, S and Kureshi, I
  • [Paper]
  • [Python Reference]

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)

  • Duvenaud, David K and Maclaurin, Dougal and Iparraguirre, Jorge and Bombarell, Rafael and Hirzel, Timothy and Aspuru-Guzik, Alan and Adams, Ryan P
  • [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)

  • Nikolentzos, Giannis and Meladianos, Polykarpos and Vazirgiannis, Michalis
  • [Paper]
  • [Matlab Reference]

• Global Weisfeiler-Lehman Graph Kernels (2017)

  • Morris, Christopher and Kersting, Kristian and Mutzel, Petra
  • [Paper]
  • [C++ Reference]

• On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)

  • Kriege, Nils M and Giscard, Pierre-Louis and Wilson, Richard
  • [Paper]
  • [Java Reference]

• Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)

  • Bonner, Stephen and Brennan, John and Theodoropoulos, G and Kureshi, I and McGough, AS
  • [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)

  • Morris, Christopher and Kriege, Nils M and Kersting, Kristian and Mutzel, Petra
  • [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)

  • Sugiyama, Mahito and Borgwardt, Karsten
  • [Paper]
  • [C++ Reference]

• Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)

  • Feragen, Aasa and Kasenburg, Niklas and Petersen, Jens and de Bruijne, Marleen and Borgwardt, Karsten
  • [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)

  • Shervashidze, Nino and Schweitzer, Pascal and Leeuwen, Erik Jan van and Mehlhorn, Kurt and Borgwardt, Karsten M
  • [Paper]
  • [Python Reference]
  • [C++ Reference]

• Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

  • Fast neighborhood subgraph pairwise distance Kernel
  • [Paper]
  • [C++ Reference]
  • [Python Reference]

• A Linear-time Graph Kernel (ICDM 2009)

  • Hido, Shohei and Kashima, Hisashi
  • [Paper]
  • [Python Reference]

• Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

  • Shervashidze, Nino and Schweitzer, Pascal and Leeuwen, Erik Jan van and Mehlhorn, Kurt and Borgwardt, Karsten M
  • [Paper]
  • [Python Reference]
  • [C++ Reference]

• Fast Computation of Graph Kernels (NIPS 2006)

  • Borgwardt, Karsten M and Schraudolph, Nicol N and Vishwanathan, SVN
  • [Paper]
  • [Python Reference]
  • [C++ Reference]

• Shortest-Path Kernels on Graphs (ICDM 2005)

  • Borgwardt, Karsten M and Kriegel, Hans-Peter
  • [Paper]
  • [C++ Reference]

• Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)

  • Horvath, Tamas and Gartner, Thomas and Wrobel, Stefan
  • [Paper]
  • [Python Reference]

• Extensions of Marginalized Graph Kernels (ICML 2004)

  • Mahe, Pierre and Ueda, Nobuhisa and Akutsu, Tatsuya and Perret, Jean-Luc and Vert, Jean-Philippe
  • [Paper]
  • [Python Reference]

• Marginalized Kernels Between Labeled Graphs (ICML 2003)

  • Kashima, Hisashi and Tsuda, Koji and Inokuchi, Akihiro
  • [Paper]
  • [Python Reference]