Low-Rank Projections of GCNs Laplacian release_nqdldv3ag5fjtmgmd6mpbn4c64

by Nathan Grinsztajn

Released as a article .

2021  

Abstract

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.
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Type  article
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Date   2021-06-04
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Language   en ?
arXiv  2106.07360v1
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