Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
This work addresses interpretability and performance issues in spectral GNNs for graph learning tasks, offering a novel perspective that enhances long-range dependency handling and heterophily adaptation, though it builds incrementally on existing spectral methods.
The paper tackles the lack of spatial interpretability in spectral Graph Neural Networks (GNNs) by establishing a theoretical link between spectral filtering and spatial aggregation, revealing that spectral filtering implicitly adapts the graph for non-local and signed-edge aggregation, and proposes a Spatially Adaptive Filtering (SAF) framework that improves performance on 13 node classification benchmarks.
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage to the spatial domain. As previous studies rarely examine spectral GNNs from the spatial perspective, their spatial-domain interpretability remains elusive, e.g., what information is essentially encoded by spectral GNNs in the spatial domain? In this paper, to answer this question, we establish a theoretical connection between spectral filtering and spatial aggregation, unveiling an intrinsic interaction that spectral filtering implicitly leads the original graph to an adapted new graph, explicitly computed for spatial aggregation. Both theoretical and empirical investigations reveal that the adapted new graph not only exhibits non-locality but also accommodates signed edge weights to reflect label consistency among nodes. These findings thus highlight the interpretable role of spectral GNNs in the spatial domain and inspire us to rethink graph spectral filters beyond the fixed-order polynomials, which neglect global information. Built upon the theoretical findings, we revisit the state-of-the-art spectral GNNs and propose a novel Spatially Adaptive Filtering (SAF) framework, which leverages the adapted new graph by spectral filtering for an auxiliary non-local aggregation. Notably, our proposed SAF comprehensively models both node similarity and dissimilarity from a global perspective, therefore alleviating persistent deficiencies of GNNs related to long-range dependencies and graph heterophily. Extensive experiments over 13 node classification benchmarks demonstrate the superiority of our proposed framework to the state-of-the-art models.