Latent adversarial training of graph convolution networks

ICML Workshop on Learning and Reasoning with Graph-Structured Representations, 2019.

Cited by: 3|Views8


Despite the recent success of graph convolution networks (GCNs) in modeling graph structured data, its vulnerability to adversarial attacks have been revealed and attacks on both node feature and graph structure have been designed. Direct extension of adversarial sample based defense algorithms meets with immediate challenge because compu...More



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