Billion-Scale Graph Foundation Models

TL;DR

This paper introduces GraphBFF, a scalable framework for building billion-parameter Graph Foundation Models that effectively learn from large-scale heterogeneous graphs, demonstrating superior performance across diverse tasks.

Contribution

The paper presents a novel scalable architecture and methodology for training billion-parameter GFMs on large heterogeneous graphs, including neural scaling laws and practical training recipes.

Findings

Loss decreases predictably with model and data scale.

GraphBFF outperforms baselines by up to 31 PRAUC points.

Effective across ten diverse downstream graph tasks.

Abstract

Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, extending this paradigm to general, real-world graphs is challenging. In this work, we present Graph Billion-Foundation-Fusion (GraphBFF): an end-to-end recipe for building billion-parameter Graph Foundation Models (GFMs) for large-scale heterogeneous graphs. Central to the recipe is the GraphBFF Transformer, a flexible and scalable architecture designed for practical billion-scale GFMs. Using the GraphBFF, we present neural scaling laws for heterogeneous graphs and show that loss decreases predictably as either model capacity or training data scales, depending on which factor is the bottleneck. The GraphBFF framework provides concrete methodologies for data batching, pretraining, and fine-tuning for building GFMs at scale. We demonstrate the effectiveness of the framework over a real-world billion-scale graph, with an evaluation of a billion-parameter GraphBFF Transformer following the proposed recipe. Across ten diverse, real-world downstream tasks on graphs unseen during training, spanning node- and link-level classification and regression, GraphBFF consistently outperforms baselines, with large margins of up to 31 PRAUC points, including in few-shot settings. Finally, we discuss key challenges and open opportunities for making GFMs a practical and principled foundation for graph learning at industrial scale.