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This content will become publicly available on April 24, 2026

Title: Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggregation mechanism, recent studies suggest that other components such as non-linear layers may also significantly affecting how GNNs process the input graph data in the spectral domain. Such evidence challenges the prevalent opinion that neighborhood aggregation mechanisms dominate the behavioral characteristics of GNNs in the spectral domain. To demystify such a conflict, this paper introduces a comprehensive benchmark to measure and evaluate GNNs' capability in capturing and leveraging the information encoded in different frequency components of the input graph data. Specifically, we first conduct an exploratory study demonstrating that GNNs can flexibly yield outputs with diverse frequency components even when certain frequencies are absent or filtered out from the input graph data. We then formulate a novel research problem of measuring and benchmarking the performance of GNNs from a spectral perspective. To take an initial step towards a comprehensive benchmark, we design an evaluation protocol supported by comprehensive theoretical analysis. Finally, we introduce a comprehensive benchmark on real-world datasets, revealing insights that challenge prevalent opinions from a spectral perspective. We believe that our findings will open new avenues for future advancements in this area.  more » « less
Award ID(s):
2411248 2223769 2228534 2154962 2144209 2006844
PAR ID:
10612761
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
International Conference on Learning Representations
Date Published:
Format(s):
Medium: X
Location:
Singapore
Sponsoring Org:
National Science Foundation
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