How to Protect Ourselves From Overlapping Community Detection in Social Networks

  • Liu, Dong; 
  • Yang, Guoliang; 
  • Wang, Yanwei; 
  • Jin, Hu; 
  • Chen, Enhong
Citations

WEB OF SCIENCE

28
Citations

SCOPUS

33

초록

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question "Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?" If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called name BIH. Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.

키워드

Detection algorithms; Social networking (online); Privacy; Image edge detection; Complex networks; Big Data; Optimization; Overlapping community detection; community deception; hiding; privacy protection; COMPLEX NETWORKS; PERTURBATION; ATTACK
제목
How to Protect Ourselves From Overlapping Community Detection in Social Networks
저자
Liu, Dong; Yang, Guoliang; Wang, Yanwei; Jin, Hu; Chen, Enhong
DOI
10.1109/TBDATA.2022.3152431
발행일
2022-02
유형
Article
저널명
IEEE TRANSACTIONS ON BIG DATA
권
8
호
4
페이지
894 ~ 904