​​GraphAny: A Foundation Model for Node Classification on Any Graph

by Jianan Zhao, Hesham Mostafa, Michael Galkin, Michael Bronstein, Zhaocheng Zhu, Jian Tang

🚀 We have just released a new work!

Pre-trained on one graph (Wisconsin with 120 labeled node), GraphAny generalizes to any unseen graph with arbitrary feature and label spaces - 30 new graphs - with an average accuracy of 67.26% in an inductive manner, surpassing GCN and GAT individually trained in the supervised regime.

GraphAny runs inference on a new graph as analytical solutions to LinearGNNs and enjoys the inductive (training-free) inference on arbitrary feature and label spaces. The model learns inductive attention scores for each node to fuse the predictions of multiple LinearGNNs. It adaptively predicts the most important LinearGNN channels via transforming the distances features between LinearGNNs, eg, high-pass filters are more preferred on heterophilic graphs.

Unlike LLM-based-models that can’t scale to large graphs, GraphAny efficiently can be trained on 1 graph and evaluated on 30 others—3M nodes & 244M edges—in just 10 mins. Works great on any 16GB GPU or even a CPU.

Finally, you can train a model on Cora and run inductive node classification on Citeseer, Pubmed, and actually any graph!

Paper, Code