FSC-CD: A Feature–Structure Coupled Approach for Community Deception in Networks

Abstract

Many real-world networks, such as social and biological networks, exhibit community structures. Community detection algorithms extract valuable insights from these networks by identifying densely connected groups, enabling applications such as recommendation, behavior understanding, and system optimization. However, growing concerns about data privacy and security have led to techniques that protect user information from being over-inferred within communities. This has given rise to community deception (CD), which introduces small, targeted perturbations to a network to obscure sensitive communities from detection algorithms. Most existing community deception approaches focus on modifying network topology, often neglecting the rich feature information embedded within communities. In this paper, we propose FSC-CD (Feature-Structure Coupled Community Deception), which couples feature-derived representations with structural cues to improve community concealment. FSC-CD is effective for both single-community deception and randomized multi-community hiding. A key innovation is a budget allocation strategy that optimizes the distribution of perturbations to maximize deception efficiency. Moreover, by exploiting feature similarity, FSC-CD designs an edge perturbation mechanism that improves stability under small perturbation budgets. Extensive experiments on three real-world network datasets across multiple community detectors show that FSC-CD is more stable and consistently outperforms baseline methods in hiding both single and multiple communities, reducing the detection accuracy by up to 17.6 % compared to state-of-the-art approaches.