A Pure Hierarchical Spectral Parcellation Network for Brain Network Analysis

Brain network classification is pivotal for diagnosing neurological disorders, yet clinical interpretability and the identification of discriminative biomarkers fundamentally rely on precise functional parcellation. However, existing graph learning models for brain network analysis typically suffer from a critical limitation termed spectral unreachability. This stems from the widely used coupled encoder-pooling architecture, where the inherent representation smoothing property of graph encoders (including Graph Neural Networks and Graph Transformers) inevitably corrupts the high-frequency topological signals essential for delineating sharp functional boundaries. To solve this issue, the Hierarchical Spectral Parcellation Network (HiSP-Net) is proposed. Adopting a project-then-align philosophy, HiSP-Net structurally decouples partition learning from representation smoothing. Specifically, this model is constructed as a hierarchy of Spectral Parcellation blocks. Within each block, node (or module) representations are mapped directly via a topology-agnostic projection into a partition space to preserve high-frequency details, while a Topology-Aware Alignment mechanism enforces spatial coherence using a joint structural objective. Extensive evaluations on real-world datasets show the capability of HiSP-Net in achieving superior classification performance and extracting interpretable functional biomarkers. The source code is publicly available at https://anonymous.4open.science/r/HiSP-Net-demo-0F62/