Disentangling Homophilic and Heterophilic Patterns for Multi-Domain Graph Foundation Models

Graph Foundation Models (GFMs) have attracted growing attention for their ability to model multi-domain graphs universally and generalize to unseen downstream tasks. However, existing GFMs typically rely on a single domain-invariant representation space, which inevitably blurs two fundamental connectivity patterns, homophily and heterophily, that govern how nodes relate across domains. Additionally, hard-partitioning the graph into disjoint subgraphs discards edges that are essential for self-supervised pre-training. In this work, we introduce $\underline{\text{HHGFM}}$, a $\underline{\text{H}}$omophilic and $\underline{\text{H}}$eterophilic Pattern-Disentangled $\underline{\text{G}}$raph $\underline{\text{F}}$oundation $\underline{\text{M}}$odel that encodes homophily and heterophily separately while keeping the original edge structure intact. Specifically, we propose a node-level soft disentanglement mechanism that estimates each neighbor's homophilic and heterophilic contributions and uses them as edge weights, thereby preserving the original graph structure. Low-pass and high-pass filters then serve as dual backbones to align homophilic and heterophilic patterns separately, supported by our generalization analysis. Extensive experiments on a range of graph datasets validate the effectiveness of our method.