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2 Result(s)
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Chapter and Conference Paper
An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning
Mining graph data has gained wide attention in modern applications. With the explosive growth of graph data, it is common to see many of them collected and stored in different distinction systems. These local ...
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Chapter and Conference Paper
Learning Robust Representation Through Graph Adversarial Contrastive Learning
Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features. Thus, i...
