Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017, Article ID 4783159, 16 pages
https://doi.org/10.1155/2017/4783159
Research Article

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China
2Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 211106, China

Correspondence should be addressed to Dechang Pi; nc.ude.aaun@ip.cd

Received 7 June 2017; Accepted 12 November 2017; Published 4 December 2017

Academic Editor: Jia Wu

Copyright © 2017 Yufei Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.