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CDNE: Community deception from node and edge perspectives
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AI
DOI：10.1016/j.neucom.2026.133039.png)
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Abstract
En 中文
As complex networks grow, community detection aids social network clustering but also risks exposing sensitive ties. Community deception alters network structures to hide target communities and protect privacy. Existing deception approaches primarily rely on either node-level or edge-level interventions, yet they often neglect the heterogeneous influence of individual nodes and edges, resulting in suboptimal concealment performance. To address these limitations, we propose CDNE, a novel community deception model from both node and edge perspectives. The model integrates node-based community deceptions into edge-based community deceptions for the first time, thus expanding the feasible manipulation space and enabling more flexible and effective deceptions. Moreover, we theoretically study the effects of inter-community and intra-community edge adding and deleting operations as a deception optimization function. Experiments on twenty-five community structure partitions generated by five real-world network datasets and five community detection algorithms show that CDNE consistently outperforms existing state-of-the-art deception methods.
Keywords:
Community deception
Complex networks
Privacy protection
Node-level intervention
Edge-level intervention
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