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Most influential community search over large social networks

conference contribution
posted on 2017-01-01, 00:00 authored by Jianxin LiJianxin Li, Xinjue Wang, Ke Deng, Xiaochun Yang, Timos Sellis, Jeffrey Xu Yu
Detecting social communities in large social networks provides an effective way to analyze the social media users' behaviors and activities. It has drawn extensive attention from both academia and industry. One essential aspect of communities in social networks is outer influence which is the capability to spread internal information of communities to external users. Detecting the communities of high outer influence has particular interest in a wide range of applications, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery. However, the existing detection techniques largely ignore the outer influence of the communities. To fill the gap, this work investigates the Most Influential Community Search problem to disclose the communities with the highest outer influences. We firstly propose a new community model, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum. Then, we design a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential communities, we also develop four advanced index-based algorithms which improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of our solution have been extensively verified using six real datasets and a small case study.

History

Event

Data Engineering. Conference (2017 : 33rd : San Diego, California)

Pagination

871 - 882

Publisher

IEEE

Location

San Diego, California

Place of publication

Piscataway, N.J.

Start date

2017-04-19

End date

2017-04-22

ISBN-13

9781509065431

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

ICDE 2017 : Proceedings of the IEEE 33rd International Conference on Data Engineering

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