Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2016

Abstract

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.

Keywords

Community detection, Dynamic, Heuristic, Modularity

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

Web technologies and applications: 18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China, September 23-25: Proceedings

Volume

9932

First Page

478

Last Page

482

ISBN

9783319458168

Identifier

10.1007/978-3-319-45817-5_50

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-319-45817-5_50

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