Deperturbation of Online Social Networks via Bayesian Label Transition

Jun Zhuang, Mohammad Al Hasan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such a task can be solved effectively using Graph Convolutional Networks (GCNs). However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task. Existing works in this direction defend GCNs either by adversarial training or by identifying the attacker nodes followed by their removal. However, both of these approaches require that the attack patterns or attacker nodes be identified first, which is difficult in the scenario when the number of perturbator nodes is very small. In this work, we develop a GCN defense model, namely GraphLT 1 , which uses the concept of label transition. GraphLT assumes that perturbators’ random activities deteriorate GCN’s performance. To overcome this issue, GraphLT subsequently uses a novel Bayesian label transition model, which takes GCN’s predicted labels and applies label transitions by Gibbs-sampling-based inference and thus repairs GCN’s prediction to achieve better node classification. Extensive experiments on seven benchmark datasets show that GraphLT considerably enhances the performance of the node classifier in an unperturbed environment; furthermore, it validates that GraphLT can successfully repair a GCN-based node classifier with superior performance than several competing methods.

Original languageEnglish
Title of host publicationProceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022
PublisherSociety for Industrial and Applied Mathematics Publications
Pages603-611
Number of pages9
ISBN (Electronic)9781611977172
DOIs
StatePublished - 2022
Event2022 SIAM International Conference on Data Mining, SDM 2022 - Virtual, Online
Duration: 28 Apr 202230 Apr 2022

Publication series

NameProceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022

Conference

Conference2022 SIAM International Conference on Data Mining, SDM 2022
CityVirtual, Online
Period28/04/2230/04/22

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