Efficient community maintenance for dynamic social networks
Conference Proceeding Article
Community detection plays an important role in a wide range of research topics for social networks including personalized recommendation services and information dissemination. The highly dynamic nature of social platforms, and accordingly the constant updates to the underlying network, all present a serious challenge for efficient maintenance of the identified communities. How to avoid computing from scratch the whole community detection result in face of every update, which constitutes small changes more often than not. To solve this problem, we propose a novel and efficient algorithm to maintain the communities in dynamic social networks by identifying and updating only those vertices whose community memberships are accepted. The complexity of our algorithm is independent of the graph size. Experiments across varied datasets demonstrate the superiority of our proposed algorithm in terms of time efficiency and accuracy.
Community detection, Dynamic, Heuristic, Modularity
Communication | Social Media
Data Management and Analytics
APWeb 2016: Proceedings, Part II: 18th Asia-Pacific Web Conference, Suzhou, China, 2016 September 23-25.
Springer International Publishing
City or Country
QIN, Hongchao; YUAN, Ye; ZHU, Feida; and WANG, Guoren.
Efficient community maintenance for dynamic social networks. (2016). APWeb 2016: Proceedings, Part II: 18th Asia-Pacific Web Conference, Suzhou, China, 2016 September 23-25.. 9932, 478-482. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3448