Git Product home page Git Product logo

awesome-graph-embedding's Introduction

Awesome Graph Embedding And Representation Learning Papers

Awesome PRs Welcome

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

The task is to learn features and representations of graphs from a graph database. These are later used for supervised learning.

Paper References with the implementation(s)

(Implicit) Factorization machines

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

  • 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]

  • Deep Learning with Topological Signatures (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]

  • 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]

Kernel Methods

  • 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)

  • 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)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.