Community Deception or: How to Stop Fearing Community Detection Algorithms

• Published in: IEEE transactions on knowledge and data engineering Vol. 30; no. 4; pp. 660 - 673
• Main Authors: Fionda, Valeria, Pirro, Giuseppe
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Communities; Community deception; community detection; community hiding; Deception; Deception techniques; Detection algorithms; Facebook; Image edge detection; Knowledge engineering; Modularity; Police; Robustness; Social networks
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2017.2776133

In this paper, we research the community deception problem. Tackling this problem consists in developing techniques to hide a target community (C) from community detection algorithms. This need emerges whenever a group (e.g., activists, police enforcements, or network participants in general) want to observe and cooperate in a social network while avoiding to be detected. We introduce and formalize the community deception problem and devise an efficient algorithm that allows to achieve deception by identifying a certain number (b) of C's members connections to be rewired. Deception can be practically achieved in social networks like Facebook by friending or unfriending network members as indicated by our algorithm. We compare our approach with another technique based on modularity. By considering a variety of (large) real networks, we provide a systematic evaluation of the robustness of community detection algorithms to deception techniques. Finally, we open some challenging research questions about the design of detection algorithms robust to deception techniques.