The vital principle of GSP-GNN is to explore the similarity property to mitigate negative effects on graphs. Specifically, this method prunes adversarial edges by the similarity of node feature and graph structure to eliminate adversarial perturbations.
Feb 9, 2023 · The vital principle of GSP-GNN is to explore the similarity property to mitigate negative effects on graphs. Specifically, this method prunes ...
Jan 1, 2023 · Specifically, this method prunes adversarial edges by the similarity of node feature and graph structure to eliminate adversarial perturbations.
GSP-GNN is proposed, a general framework to defend against massive poisoning attacks that can perturb graphs and prunes adversarial edges by the similarity ...
This paper introduces a novel tensor-based framework for GNNs, aimed at reinforcing graph robustness against adversarial influences.
Its core principle is to detect and quantify the relationship between the graph structure and node features, if one exists, and then exploit that relationship ...
GNNGuard is a general defense approach against a variety of poisoning adversarial attacks that perturb the discrete graph structure.
Missing: property. | Show results with:property.
Dec 3, 2024 · ... (Defending against adversarial attacks on graph neural networks via similarity property). Citation metadata. Date: Feb. 13, 2023. From: Journal ...
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Its core principle is to detect and quantify the relationship between the graph structure and node features, if one exists, and then exploit that relationship ...
Missing: via property.
Dec 25, 2024 · To defend against these adversarial attacks, there are an increasing number of studies that attempt to protect the GNN model architectures or ...