Attribute-Topology Cross-Frequency Aligned Graph Neural Networks for Homophilic and Heterophilic Graphs in Node Classification.

Graph neural networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest. However, two primary challenges have emerged: interference between topology and attributes distorting node representations, and the low-pass filtering nature of most GNNs leading to the oversight of valuable high-frequency information in graph signals. These issues are particularly pronounced in heterophilic graphs. To address these challenges, we propose attribute-topology cross-frequency aligned (ATCFA) GNNs. ATCFA combines low- and high-pass filters to capture both smooth and detailed representations from topological and attribute perspectives. It also enforces frequency-specific constraints to reduce noise and redundancy in each frequency band. The model can dynamically adjust the filtering ratios for both homophilic and heterophilic graphs. Crucially, ATCFA establishes dynamic associations between corresponding frequency components of topology and attribute, achieving systematic alignment and interactive fusion that explicitly mitigates interference and promotes complementary information utilization across domains. Extensive experiments on standard datasets show that ATCFA delivers higher classification accuracy than state-of-the-art methods, proving its capability to handle both homophilic and heterophilic graphs in node classification.