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Proposes ACE, a plug-and-play method that adaptively enhances coarsening-based GNN training on heterophilic graphs by reconstructing node features and applying anisotropic regularization, achieving consistent gains on heterophilic benchmarks with minimal overhead.
This paper introduces HAAM, a novel method for node classification in multiplex graphs that adapts to both homophilic and heterophilic interactions across dimensions. It uses dimension-specific compatibility matrices and a product of trainable low-pass and high-pass filters approximated via Chebyshev polynomials to capture smooth and abrupt signal changes.
This paper introduces HMH, a hierarchical multi-scale Graph Neural Network framework designed to address oversmoothing and oversquashing in heterophilous graphs. It utilizes spectral filters with Haar bases to achieve scalable learning and improved performance on node and graph classification tasks.