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Temporal interaction biased community detection in social networks

conference contribution
posted on 2016-01-01, 00:00 authored by Noha Alduaiji, Jianxin LiJianxin Li, Amitava Datta, Xiaolu Lu, Wei Liu
Community detection in social media is a fundamental problem in social data analytics in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, most of the research examined communities based on static structure in social networks. Our findings within large social networks such as Twitter, show that only a few users have interactions or communications within any fixed time interval. It is not difficult to see that it makes more potential sense to find such active communities that are biased to temporal interactions of social users, rather than relying solely on static structure. Communities detected with this new perspective will provide time-variant social relationships or recommendations in social networks, which can greatly improve the applicability of social data analytics. In this paper, we address the proposed problem of temporal interaction biased community detection using a three-step process. Firstly, we develop an activity biased weight model which gives higher weight to active edges or inactive edges in close proximity to active edges. Secondly, we redesign the activity biased community model by extending the classical density based community detection metric. Thirdly, we develop two different expansion-driven algorithms to find the activity biased densest community efficiently. Finally, we verify the effectiveness of the extended community metric and the efficiency of the algorithms using three real datasets.

History

Volume

10086

Pagination

406-419

Location

Gold Coast, Qld.

Start date

2016-12-12

End date

2016-12-15

ISSN

0302-9743

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2016, Springer International Publishing AG

Editor/Contributor(s)

Li J, Li X, Wang S, Li J, Sheng QZ

Title of proceedings

ADMA 2016 : Proceedings of the 12th International Conference on Advanced Data Mining and Applications 2016

Event

Advanced Mining and Applications. Conference (12th : 2016 : Gold Coast, Qld.)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Advanced Mining and Applications Conference

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