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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 LiuCommunity 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.
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
Event
Advanced Mining and Applications. Conference (12th : 2016 : Gold Coast, Qld.)Volume
10086Series
Advanced Mining and Applications ConferencePagination
406 - 419Publisher
SpringerLocation
Gold Coast, Qld.Place of publication
Cham, SwitzerlandPublisher DOI
Start date
2016-12-12End date
2016-12-15ISSN
0302-9743Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2016, Springer International Publishing AGEditor/Contributor(s)
Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan ShengTitle of proceedings
ADMA 2016 : Proceedings of the 12th International Conference on Advanced Data Mining and Applications 2016Usage metrics
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