Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks

Lianren Wu, Jiayin Qi, Nan Shi, Jinjie Li and Qiang Yan

Physica A: Statistical Mechanics and its Applications, 2022, vol. 588, issue C

Abstract: In social networks, how human activity patterns affect the popularity of topics has always been the focus of research. In this paper, a quantitative temporal analysis of the dynamics of topics popularity in Sina Weibo system was provided. Firstly, the popularity time series of 1167 topics were clustered into four clusters by K-Spectral Centroid (K-SC) clustering algorithm. Secondly, for each cluster, we calculated the exponents of topic popularity decay distribution α and the exponents of inter-activity time distribution β, respectively. Two interesting results were found: one is that the peak fraction F of topics popularity positively correlated with the topics popularity decay exponent α; the other is that bursty activity patterns in social network significantly affect topics popularity dynamics: there is a positive correlation between exponent α and exponent β. Finally, we proposed an extended SI (susceptible–infected) epidemic model with incorporate bursty human activity and verified the results by simulation.

Keywords: Human dynamics; Social temporal networks; Topic popularity; Bursty activity patterns (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:588:y:2022:i:c:s0378437121008414

DOI: 10.1016/j.physa.2021.126568

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