title: Characterizing and modeling the dynamics of activity and popularity.
abstract: Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing, P. R. China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing, P. R. China ; Beijing Institute of Science and Technology Intelligence, Beijing, P. R. China.
authors.forename: Menghui
authors.initials: M
authors.lastname: Li
authors.affiliation: Systems Science Institute and MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, P. R. China.
authors.forename: Liang
authors.initials: L
authors.lastname: Gao
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing, P. R. China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Fan
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing, P. R. China.
authors.forename: Zengru
authors.initials: Z
authors.lastname: Di
mesh_terms: D006801:Humans; D020407:Internet; D008962:Models, Theoretical; D012929:Psychological Distance; D061108:Social Media; D060756:Social Networking