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A Survey on Spectral Graph Neural Networks
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Graph neural networks (GNNs) have attracted considerable attention from the research community. It is well established that GNNs are usually roughly divided into spatial and spectral methods. Despite that spectral GNNs play an important role in both graph signal processing and graph representation learning, existing studies are biased toward spatial approaches, and there is no comprehensive review on spectral GNNs so far. In this paper, we summarize the recent development of spectral GNNs, including model, theory, and application. Specifically, we first discuss the connection between spatial GNNs and spectral GNNs, which shows that spectral GNNs can capture global information and have better expressiveness and interpretability. Next, we categorize existing spectral GNNs according to the spectrum information they use, \ie, eigenvalues or eigenvectors. In addition, we review major theoretical results and applications of spectral GNNs, followed by a quantitative experiment to benchmark some popular spectral GNNs. Finally, we conclude the paper with some future directions.
Forward citations
Cited by 6 Pith papers
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Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
Graph condensation methods must move beyond full-dataset training and model dependence toward lightweight, architecture-agnostic designs to achieve practical efficiency in GNNs.
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