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Found 2 papers, 1 papers with code
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data
In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal.
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding
The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel.
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