Abstract and Applied Analysis
Volume 2012 (2012), Article ID 208791, 16 pages
http://dx.doi.org/10.1155/2012/208791
Research Article

Analyzing, Modeling, and Simulation for Human Dynamics in Social Network

Yunpeng Xiao,1,2 Bai Wang,1 Yanbing Liu,2 Zhixian Yan,3 Xian Chen,4 Bin Wu,1 Guangxia Xu,2 and Yuanni Liu2

1Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications (BUPT), Beijing, China
2Chongqing Engineering Laboratory of Internet and Information Security, Chongqing University of Posts and Telecommunications (CQUPT), no. 2 Chongwen Road, Nanan District, Chongqing 400065, China
3Samsung Research, San Jose, CA, USA
4Web Intelligence Laboratory, Konkuk University, Seoul, Republic of Korea

Received 25 October 2012; Accepted 4 December 2012

Academic Editor: Chuandong Li

Copyright © 2012 Yunpeng Xiao 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

This paper studies the human behavior in the top-one social network system in China (Sina Microblog system). By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time) obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.