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- W4377990503 abstract "Graph data are widely available in complex network systems. Numerous community detection algorithms have been investigated to study graph problems wherein the network topology offers abundant behavioral and functional information. In real scenarios, networks are extremely susceptible to external perturbations, primarily because of sparse topological information. Consequently, maintaining robust community detection performance when confronted with intricate network attacks or perturbations is challenging. Previous anti-graph perturbation methods have typically been adjusted to specific perturbation types, leading to the degradation or failure of these defences when confronted with other types of attack. Accordingly, a unified robust framework that leverages extreme adversarial attacks is proposed in this paper. Specifically, a novel graph perturbation module is introduced into the model to generate extreme undirected or directed perturbations through dynamic updating; this ensures that the model can fit the attacked network well. The proposed framework derives a unified perturbation formulation capable of simultaneously attacking symmetric and asymmetric networks. Furthermore, this framework can be applied to many existing non-negative matrix factorization-based community detection methods. Extensive experiments on artificial and real-world networks demonstrate that the proposed framework significantly improves the robustness of detection tasks, particularly in noisy networks." @default.
- W4377990503 created "2023-05-25" @default.
- W4377990503 creator A5013240931 @default.
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- W4377990503 creator A5082612941 @default.
- W4377990503 date "2023-09-01" @default.
- W4377990503 modified "2023-10-17" @default.
- W4377990503 title "Unified robust network embedding framework for community detection via extreme adversarial attacks" @default.
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- W4377990503 doi "https://doi.org/10.1016/j.ins.2023.119200" @default.
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