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Sparse Vicious Attacks on Graph Neural Networks. (arXiv:2209.09688v1 [cs.LG])
Sept. 21, 2022, 1:20 a.m. | Giovanni Trappolini, Valentino Maiorca, Silvio Severino, Emanuele Rodolà, Fabrizio Silvestri, Gabriele Tolomei
cs.CR updates on arXiv.org arxiv.org
Graph Neural Networks (GNNs) have proven to be successful in several
predictive modeling tasks for graph-structured data.
Amongst those tasks, link prediction is one of the fundamental problems for
many real-world applications, such as recommender systems.
However, GNNs are not immune to adversarial attacks, i.e., carefully crafted
malicious examples that are designed to fool the predictive model.
In this work, we focus on a specific, white-box attack to GNN-based link
prediction models, where a malicious node aims to appear in …
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