Cross-Scale Meta-Path Learning for Heterogeneous Brain Network Analysis

The brain is a complex system of interconnected neurons that can be modeled as a brain network. Brain disorders often involve abnormal functional connectivity, making it important to jointly capture local interactions among brain regions and high-order coordination across functional subnetworks. However, existing brain graph learning methods mainly rely on direct pairwise connections or entangled multi-hop propagation, limiting their ability to distinguish heterogeneous connection semantics and explicitly model high-order cross-subnetwork communication. To address these issues, we propose a meta-path-guided cross-scale heterogeneous brain network learning framework. Specifically, we design a low-order heterogeneous communication module that separately models intra-subnetwork and inter-subnetwork connections through relation-specific message passing and subnetwork-aware attention. We further introduce a high-order meta-path communication module that constructs high-order communication structures within a unified functional-subnetwork meta-path space. By adaptively selecting disease-discriminative meta-paths, the module captures high-order cross-subnetwork interactions. In addition, node-level cross-scale contrastive learning is employed to align low-order and high-order representations and promote their complementarity. Experimental results on the ADNI and ABIDE datasets demonstrate that the proposed method outperforms existing approaches and identifies informative brain regions and meta-paths.