[TOC]

• About
• Template
  • A Possible Subfield
• Expressive Power
• Pre-training / Self-supervised Learning
• Deep GNNs
• Scalable GNNs
• Hierarchical GNNs
• Dynamic GNNs
• Hypergraph Neural Networks
• GNNs for Neural Relational Inference

About

Focused areas in graph neural networks (GNNs). Written in both English and Chinese. Contributions are welcomed!

Template

A Possible Subfield

• Title in bold font. Authors in italic font. Journal / Conference year in normal font.
  • resources: paper (must), code (optional), slides (optional), project page (optional), review (optional), blogs (optional), my note (optional)
  • contributions (must)
• Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. ICLR 2017.
  • resources: paper, code, review, blog
  • contributions:
    • A simple and numerically stable GCN in the spectral domain.
    • Pointing out the connection between GCN and 1st-order Weisfeiler-Lehma test.
    • Empirically showing that the performance may degenerate as GCN goes deep.

Expressive Power

• Intro: Expressive power of GNNs and permutation equivariant GNNs.
• Survey: A Survey on The Expressive Power of Graph Neural Networks. Ryoma Sato. CoRR 2020. paper
• Notes: cnblogs

Pre-training / Self-supervised Learning

• Intro：Pre-training GNNs or training GNNs in a self-supervised manner to allow better generalization.
• Notes: cnblogs

Deep GNNs

• Intro: Analyzing the over-smoothing problem of GNNs and explore possible solutions to make GNNs deep.

Scalable GNNs

• Intro: Applying GNNs to large-scale graphs.

Hierarchical GNNs

• Intro: Learning hierarchical representations of graphs and developing pooling methods for GNNs.

Dynamic GNNs

• Intro: Designing GNNs for dynamic graphs whose graph structure and attributes vary over time.
• Survey: Representation Learning for Dynamic Graphs: A Survey. JMLR 2020. paper

Hypergraph Neural Networks

GNNs for Neural Relational Inference

• Intro: Jointly inferring the interacting relations and learning the dynamics of dynamical systems in an unsupervised manner.
• First paper: Neural Relational Inference for Interacting Systems. ICML 2018. paper