PLoS One. 2014 Nov 7;9(11):e111013. doi: 10.1371/journal.pone.0111013. eCollection 2014.

Emergence, Evolution and Scaling of Online Social Networks.

Wang LZ1, Huang ZG2, Rong ZH3, Wang XF4, Lai YC5.

Author information

• 1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, United States of America.
• 2School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, United States of America; School of Physical Science and Technology, Lanzhou University, Lanzhou, China.
• 3Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, China.
• 4Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
• 5School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, United States of America; Department of Physics, Arizona State University, Tempe, Arizona, United States of America.

Abstract

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.