“Vertex classification — the problem of identifying the class labels of nodes in a graph — has applicability in a wide variety of domains. Examples include classifying subject areas of papers in citation networks or roles of machines in a computer network. Vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target node. … This paper considers an alternative: we leverage network characteristics in the training data selection process to improve robustness of vertex classifiers.”

Find the paper and list of authors at ArXiv.