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ConanCui / awesome-graph-embedding

A collection of important graph embedding, classification and representation learning papers with implementations.

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Awesome Graph Embedding

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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

Spectral and Statistical Fingerprints

  • NetLSD (KDD 2018)

    • Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Bronstein, Alex and Muller, Emmanuel
    • [Paper]
    • [Python Reference]

  • Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)

  • 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)

  • MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)

  • Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)

  • Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)

  • Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)

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

  • 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)

  • Deep Learning with Topological Signatures (NIPS 2017)

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

  • Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)

  • 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)

  • 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)

  • 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)

  • Matching Node Embeddings for Graph Similarity (AAAI 2017)

  • Global Weisfeiler-Lehman Graph Kernels (2017)

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

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

  • The Multiscale Laplacian Graph Kernel (NIPS 2016)

  • Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)

  • Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)

  • Halting Random Walk Kernels (NIPS 2015)

  • 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)

  • Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)

  • Weisfeiler-Lehman Graph Kernels (JMLR 2011)

  • Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

  • A Linear-time Graph Kernel (ICDM 2009)

  • Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

  • Fast Computation of Graph Kernels (NIPS 2006)

  • Shortest-Path Kernels on Graphs (ICDM 2005)

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

  • 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)

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A collection of important graph embedding, classification and representation learning papers with implementations.

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