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

Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L Dyer

Keywords: graph transformer, multi-graph training, graph foundation model, node classification

TL;DR: This work introduces GraphFM, an approach designed for learning across diverse graph datasets, allowing generalization and strong performance across multiple domains with a single model.

Abstract: 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. We propose new techniques for scaling up graph training on datasets of different sizes, allowing us to train GraphFM on 152 distinct graph datasets, spanning 7.4 million nodes and 189 million edges. This allows us to study the effect of scale on pretraining across domains such as molecules, citation networks, and product graphs, and show that training on diverse datasets improves performance over single-source pretraining. Our results demonstrate that pretraining on diverse real and synthetic graphs enhances adaptability and stability, leading to competitive performance with state-of-the-art models across various node classification tasks. This approach reduces the burden of dataset-specific training and provides a single generalist model capable of performing across multiple diverse graph structures and tasks.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 11732