GraphFM: A generalist graph transformer that learns transferable representations across diverse domains

Understand

Graph neural networks (GNNs) are often trained on individual datasets, requiring specialized models and significant hyperparameter tuning due to the unique structures and features of each dataset.

• This approach limits the scalability and generalizability of GNNs, as models must be tailored for each specific graph type.
• To address these challenges, we introduce GraphFM, a scalable multi-graph pretraining approach designed for learning across diverse graph datasets.
• GraphFM uses a Perceiver-based encoder with learned latent tokens to compress domain-specific features into a shared latent space, enabling generalization across graph domains.