Graph Adversarial Networks: Protecting Information against Adversarial Attacks

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Abstract:

We explore the problem of protecting information when learning with graph-structured data. While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representational learning in many applications, the neighborhood aggregation paradigm exposes additional vulnerabilities to attackers seeking to extract node-leve...More

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