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Low-Rank Projections of GCNs Laplacian

arXiv 2106.07360 ↗First posted 4 Jun 2021cs.SI cs.LGcited by 0

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Abstract (arXiv)

In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the performance of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to images, high frequencies are less crucial to community detection. In particular, it is sometimes possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 4 Jun 2021.

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References · 25

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13 more
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  13. 2020Spectral Embedding of Graph Networkscited by 1

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