Subgraph sampling-based adversarial attack method for large-scale graphs
A subgraph sampling-based adversarial example generation method was proposed to enhance the efficiency of adversarial attacks on large-scale graphs.PageRank,cosine similarity,and K-hop subgraphs were employed to extract subgraphs highly relevant to the target node in this method,alleviating the issue of low gradient computation efficiency in large-scale graphs.The stealthiness of the attack was also increased while reducing the accuracy of the attacked model.Experimental results showed that attack performance was improved by 30.7%on the Cora dataset by this adversarial attack method compared to the GradArgmax algorithm,and attack perturbations on large-scale like Reddit dataset could be computed which the GradArgmax algorithm could not achieve.
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