Complex network effects on the robustness of graph convolutional networks

Complex network effects on the robustness of graph convolutional networks

Abstract 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 vulnerability decreases users' confidence in the learning method and...

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Bibliographic Details
Main Authors:	Benjamin A. Miller, Kevin Chan, Tina Eliassi-Rad
Format:	Article
Language:	English
Published:	SpringerOpen 2024-02-01
Series:	Applied Network Science
Subjects:	Graph convolutional networks Poisoning attacks Robust machine learning
Online Access:	https://doi.org/10.1007/s41109-024-00611-9

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