title: Emergence of bursts and communities in evolving weighted networks.
abstract: Understanding the patterns of human dynamics and social interaction and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.g., the number of calls between two users, and the network topology are found correlated such that intra-community links are stronger compared to the weak inter-community links. This feature is known as Granovetter's "The strength of weak ties" hypothesis. In addition to this inhomogeneous community structure, the temporal patterns of human dynamics turn out to be inhomogeneous or bursty, characterized by the heavy tailed distribution of time interval between two consecutive events, i.e., inter-event time. In this paper, we study how the community structure and the bursty dynamics emerge together in a simple evolving weighted network model. The principal mechanisms behind these patterns are social interaction by cyclic closure, i.e., links to friends of friends and the focal closure, links to individuals sharing similar attributes or interests, and human dynamics by task handling process. These three mechanisms have been implemented as a network model with local attachment, global attachment, and priority-based queuing processes. By comprehensive numerical simulations we show that the interplay of these mechanisms leads to the emergence of heavy tailed inter-event time distribution and the evolution of Granovetter-type community structure. Moreover, the numerical results are found to be in qualitative agreement with empirical analysis results from mobile phone call dataset.
authors.affiliation: Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Espoo, Finland. joh1@aalto.fi
authors.forename: Hang-Hyun
authors.initials: HH
authors.lastname: Jo
authors.forename: Raj Kumar
authors.initials: RK
authors.lastname: Pan
authors.forename: Kimmo
authors.initials: K
authors.lastname: Kaski
mesh_terms: D000465:Algorithms; D040421:Cell Phone; D033062:Friends; D006801:Humans; D007398:Interpersonal Relations; D015233:Models, Statistical; D012919:Social Behavior; D060756:Social Networking