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Freator / 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.

Relevant graph classification benchmark datasets are available [here].

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

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

Factorization

Spectral and Statistical Fingerprints

  • NetLSD (KDD 2018)

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

  • Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)

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

  • Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)

  • 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)

  • Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)

  • Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)

  • Graph Capsule Convolutional Neural Networks (ICML 2018)

  • Graph Classification Using Structural Attention (KDD 2018)

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

  • Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)

  • Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)

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

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

  • Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)

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

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

  • Residual Gated Graph ConvNets (ICLR 2018)

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

  • 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)

  • CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)

  • Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)

  • 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)

  • 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)

  • Matching Node Embeddings for Graph Similarity (AAAI 2017)

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

  • 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)

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

  • 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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