/awesome-graph-classification

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

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Awesome Graph Classification

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

Relevant graph classification benchmark datasets are available [here].

Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.

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

Factorization

Spectral and Statistical Fingerprints

  • A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)

  • 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

  • GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)

  • Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (NeurIPS 2019)

  • Molecule Property Prediction Based on Spatial Graph Embedding (Journal of Cheminformatics Models 2019)

  • Fast Training of Sparse Graph Neural Networks on Dense Hardware (Arxiv 2019)

  • Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)

  • Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)

  • Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification (ECML-PKDD 2019)

  • Relational Pooling for Graph Representations (ICML 2019)

  • Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structure (ICML 2019)

  • Self-Attention Graph Pooling (ICML 2019)

  • Variational Recurrent Neural Networks for Graph Classification (ICLR 2019)

  • Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)

  • Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)

  • Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)

  • Capsule Graph Neural Network (ICLR 2019)

  • How Powerful are Graph Neural Networks? (ICLR 2019)

  • 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 Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)

  • Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction (NIPS 2019)

  • Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters 2018)

  • Machine Learning for Organic Cage Property Prediction (Chemical Matters 2018)

  • 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)

  • Compound-Protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 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)

  • Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 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)

  • Learning Convolutional Neural Networks for Graphs (ICML 2016)

  • Gated Graph Sequence Neural Networks (ICLR 2016)

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

Graph Kernels

  • A Persistent Weisfeiler–Lehman Procedure for Graph Classification (ICML 2019)

  • 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)

  • A Graph Kernel Based on the Jensen-Shannon Representation Alignment (IJCAI 2015)

  • An Aligned Subtree Kernel for Weighted Graphs (ICML 2015)

    • Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock
    • [Paper]

  • 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)

  • Two New Graphs Kernels in Chemoinformatics (Pattern Recognition Letters 2012)

  • Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

  • Graph Kernels (JMLR 2010)

  • A Linear-time Graph Kernel (ICDM 2009)

  • Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

  • Kernel on Bag of Paths For Measuring Similarity of Shapes (InESANN 2007)

  • Fast Computation of Graph Kernels (NIPS 2006)

  • Shortest-Path Kernels on Graphs (ICDM 2005)

  • Graph Kernels for Chemical Informatics (Neural Networks 2005)

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

  • Extensions of Marginalized Graph Kernels (ICML 2004)

  • Extensions of Marginalized Graph Kernels (ICML 2004)

  • Marginalized Kernels Between Labeled Graphs (ICML 2003)

  • On Graph Kernels: Hardness Results and Efficient Alternatives (Learning Theory and Kernel Machines 2003)