While exploring the docs/ folder, there is substantial content across architecture, concepts, configuration, and tooling. However, it's not clear what a new user should read first to achieve a quick, ...
Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...
This is a PyTorch implementation of the GraphATA algorithm, which tries to address the multi-source domain adaptation problem without accessing the labelled source graph. Unlike previous multi-source ...
Abstract: The ubiquity of Graph Neural Networks (GNNs) emphasizes the imperative to assess their resilience against node injection attacks, a type of evasion attacks that impact victim models by ...
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