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Neural Processing Letters
Erschienen in: Neural Processing Letters 4/2022

08.02.2022

How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?

verfasst von: Jun Zhuang, Mohammad Al Hasan

Erschienen in: Neural Processing Letters | Ausgabe 4/2022

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Abstract

In recent years, it has been shown that, compared to other contemporary machine learning models, graph convolutional networks (GCNs) achieve superior performance on the node classification task. However, two potential issues threaten the robustness of GCNs, label scarcity and adversarial attacks. .Intensive studies aim to strengthen the robustness of GCNs from three perspectives, the self-supervision-based method, the adversarial-based method, and the detection-based method. Yet, all of the above-mentioned methods can barely handle both issues simultaneously. In this paper, we hypothesize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian graph noisy self-supervision model, namely GraphNS, to address both issues. Extensive experiments demonstrate that GraphNS can significantly enhance node classification against both label scarcity and adversarial attacks. This enhancement proves to be generalized over four classic GCNs and is superior to the competing methods across six public graph datasets.
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Metadaten
Titel
How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?
verfasst von
Jun Zhuang
Mohammad Al Hasan
Publikationsdatum
08.02.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10750-8

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