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Awesome ICML 2024 Graph Paper Collection

This repo contains a comprehensive compilation of graph and/or GNN papers that were accepted at the International Conference on Machine Learning 2024. Graph or Geometric machine learning possesses an indispensable role within the domain of machine learning research, providing invaluable insights, methodologies, and solutions to a diverse array of challenges and problems.

Short Overview: We've got around 250 papers focusing on Graphs and GNNs in ICML'24. The core themes of this year include equivariant GNNs, OODs, diffusions, heterophily, expressivity, and clustering. There's also a good amount of casual graph works, more than I expected. We have some very good physics-inspired research too. A lot of application papers are available, although I expected to see more in molecular, chemical GNNs and GFlowNets. Reinforcement learning also had a good boost this year. Have a look and throw me a review (and, a star ⭐, maybe!) Thanks!

All Topics:

View Topic list!

Heterophily

Hypergraph

Expressivity

Generalization

Diffusion

Clustering

Disentanglement

Others


Missing any paper? If any paper is absent from the list, please feel free to mail or open an issue or submit a pull request. I'll gladly add that! Also, If I mis-categorized, please knock!


More Collectons:


Credits

Azmine Toushik Wasi

website linkedin kaggle google-scholar facebook