title: A Multiscale Survival Process for Modeling Human Activity Patterns.
abstract: Human activity plays a central role in understanding large-scale social dynamics. It is well documented that individual activity pattern follows bursty dynamics characterized by heavy-tailed interevent time distributions. Here we study a large-scale online chatting dataset consisting of 5,549,570 users, finding that individual activity pattern varies with timescales whereas existing models only approximate empirical observations within a limited timescale. We propose a novel approach that models the intensity rate of an individual triggering an activity. We demonstrate that the model precisely captures corresponding human dynamics across multiple timescales over five orders of magnitudes. Our model also allows extracting the population heterogeneity of activity patterns, characterized by a set of individual-specific ingredients. Integrating our approach with social interactions leads to a wide range of implications.
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Tianyang
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Cui
authors.affiliation: Department of Physics, University of Miami, Miami, Florida, United States of America.
authors.forename: Chaoming
authors.initials: C
authors.lastname: Song
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Wenwu
authors.initials: W
authors.lastname: Zhu
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Shiqiang
authors.initials: S
authors.lastname: Yang
mesh_terms: D006802:Human Activities; D006801:Humans; D020407:Internet; D008962:Models, Theoretical; D011336:Probability; D016019:Survival Analysis; D013997:Time Factors