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Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 678286, 13 pages
http://dx.doi.org/10.1155/2012/678286
Research Article

A Hybrid Human Dynamics Model on Analyzing Hotspots in Social Networks

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), Room 4029, No. 2 Chongwen Road, Nanan District, Chongqing 400065, China
3School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland

Received 5 July 2012; Accepted 12 September 2012

Academic Editor: Garyfalos Papaschinopoulos

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

The increasing development of social networks provides a unique source for analyzing human dynamics in the modern age. In this paper, we analyze the top-one Internet forum in China (“Tianya Club”) and identify the statistical properties of hotspots, which can promptly reflect the crowd events in people's real-life. Empirical observations indicate that the interhotspot distribution follows a power law. To further understand the mechanism of such dynamic phenomena, we propose a hybrid human dynamic model that combines “memory” of individual and “interaction” among people. To build a rich simulation and evaluate this hybrid model, we apply three different network datasets (i.e., WS network, BA network, and Karate-Club). Our simulation results are consistent with the empirical studies, which indicate that the model can provide a good understanding of the dynamic mechanism of crowd events using such social networking data. We additionally analyze the sensitivity of model parameters and find the optimal model settings.