title: Temporal scaling in information propagation.
abstract: For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.
authors.affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Junming
authors.initials: J
authors.lastname: Huang
authors.affiliation: 1] Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China [2] Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, People's Republic of China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, People's Republic of China.
authors.forename: Wen-Qiang
authors.initials: WQ
authors.lastname: Wang
authors.affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Hua-Wei
authors.initials: HW
authors.lastname: Shen
authors.affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Guojie
authors.initials: G
authors.lastname: Li
authors.affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Xue-Qi
authors.initials: XQ
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D003142:Communication; D006801:Humans; D033181:Information Dissemination / Q000379:methods*; D007398:Interpersonal Relations; D008962:Models, Theoretical; D061108:Social Media; D013997:Time Factors