Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:COMPLEXITY

Key Words:SOCIAL NETWORK OPINION SUPERNETWORK PREDICTION SYSTEMS

Abstract:Social influence analysis is important formany 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 ofmodeling community influence and construct a community-level influence analysismodel. 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 thewillingness 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 SinaWeibo demonstrate the superiority of the proposed model.

ISSN No.:1076-2787

Translation or Not:no

Date of Publication:2017-01-01

Co-author:刘予飞,崔琳

Correspondence Author:Pi Dechang