Graph Learning Papers Guide

Overview

A curated list of graph learning papers from top AI/ML conferences (NeurIPS, ICML, ICLR, KDD, WWW, AAAI). Covers graph neural networks, graph transformers, spectral methods, message passing, and applications in molecular science, social networks, and recommendation systems. Organized by venue, year, and topic for systematic tracking.

Topic Taxonomy

Graph Learning
├── Graph Neural Networks
│   ├── Message Passing (GCN, GAT, GraphSAGE, GIN)
│   ├── Spectral (ChebNet, CayleyNet)
│   ├── Graph Transformers (Graphormer, GPS)
│   └── Equivariant GNNs (EGNN, SE(3)-Transformers)
├── Graph Generation
│   ├── VAE-based (GraphVAE)
│   ├── Autoregressive (GraphRNN)
│   ├── Diffusion (GDSS, DiGress)