  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, 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.
