README.md

Graph-based deep learning literature

The repository contains links to

• conference publications and the top 10 most cited publications
• related workshops
• surveys / literature reviews

in graph-based deep learning. The links to conference publications are arranged in the reverse chronological order of conference dates from the conferences below. Please click on a year below beside a conference name to see publications of the conference in that year.

• Machine learning conferences

  • NeurIPS - 2019 | 2018 | 2017 | 2016 | 2015

  • ICML - 2019 | 2018 | 2017 | 2016

  • ICLR - 2019 | 2018 | 2017 | 2016 | 2014

• Computer vision conferences

  • CVPR - 2019 | 2018 | 2017

  • ICCV - 2019 | 2017

  • ECCV - 2018

• Data conferences

  • KDD - 2019 | 2018

  • WWW - 2019 | 2018

  • ICDM - 2019 | 2018

• Artificial intelligence conferences

  • AAAI - 2020 | 2019 | 2018 | 2017

  • IJCAI - 2019 | 2018 | 2017

  • UAI - 2019 | 2018

• Natural language processing conferences

  • ACL - 2019 | 2018

  • EMNLP - 2019 | 2018 | 2017

  • NAACL - 2019 | 2018

Top 10 most cited publications

1. Semi-Supervised Classification with Graph Convolutional Networks

2. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

3. Spectral Networks and Locally Connected Networks on Graphs

4. Inductive Representation Learning on Large Graphs

5. Convolutional Networks on Graphs for Learning Molecular Fingerprints

6. The Graph Neural Network Model

7. Graph Attention Networks

8. Neural Message Passing for Quantum Chemistry

9. Gated Graph Sequence Neural Networks

10. Learning Convolutional Neural Networks for Graphs

Related workshops

• 2020

  • Deep Learning on Graphs: Methodologies and Applications (AAAI)

• 2019

  • Graph Representation Learning (NeurIPS)

  • Scene Graph Representation and Learning (ICCV)

  • Deep Learning on Graphs: Methods and Applications (KDD)

  • Learning and Reasoning with Graph-Structured Representations (ICML)

  • Representation Learning on Graphs and Manifolds (ICLR)

• 2018

  • Relational Representation Learning (NeurIPS)

Surveys / literature reviews

• 2019

  • Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview

  • Learning Representations of Graph Data -- A Survey

• 2018

  • Adversarial Attack and Defense on Graph Data: A Survey

  • Graph Neural Networks: A Review of Methods and Applications

  • A Comprehensive Survey on Graph Neural Networks

  • Deep Learning on Graphs: A Survey

  • Relational inductive biases, deep learning, and graph networks

• 2017

  • Representation Learning on Graphs: Methods and Applications

  • Geometric Deep Learning: Going beyond Euclidean data