Abstract

The ubiquity of text-attributed graph data has highlighted the need for graph learning models with exceptional generalization across diverse textual and structural contexts. Current approaches struggle to extract generalizable insights from heterogeneous graph data, requiring extensive fine-tuning and limiting versatility across domains. In this work, we propose AnyGraph, a unified graph foundation model designed to handle key challenges: i) Structure Heterogenity - addressing distribution shift in graph structural patterns; ii) Feature Heterogenity - handling diverse textual representations; iii) Fast Adaptation - efficiently adapting to new graph-text domains. We build AnyGraph upon a Graph Mixture-of-Experts (MoE) architecture with a lightweight expert routing mechanism that effectively manages cross-domain distribution shift. Extensive experiments on 38 diverse datasets demonstrate AnyGraph’s strong zero-shot performance across domains with significant distribution shift, validating its fast adaptation ability and scaling law emergence. Our model is open-sourced and available at: https://github.com/HKUDS/AnyGraph.