Deperturbation of Online Social Networks via Bayesian Label Transition. (arXiv:2010.14121v3 [cs.LG] UPDATED)

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 …

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