JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1021 10.6339/21-JDS1021 Statistical Data Science Common Growth Patterns for Regional Social Networks: A Point Process Approach https://orcid.org/0000-0002-7575-2229 WangTiandongtwang@stat.tamu.edu1∗ ResnickSidney I.2 Department of Statistics, Texas A&M University, College Station, TX 77843, U.S.A. School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853, U.S.A. Corresponding author. Email: twang@stat.tamu.edu. 20231692021213446469 Supplementary Material

We have collected all relevant codes and datasets, and made them available at https://github.com/tw398/NetworkGrowth. This GitHub repository includes: (1) links to the corresponding dataset on KONECT; (2) cleaned Dutch Wikipedia talk data; (3) all relevant codes for data analyses in the paper; and (4) ACF plots which are not presented in the paper.

19420211882021 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

In this paper, we study macroscopic growth dynamics of social network link formation. Rather than focusing on one particular dataset, we find invariant behavior in regional social networks that are geographically concentrated. Empirical findings suggest that the startup phase of a regional network can be modeled by a self-exciting point process. After the startup phase ends, the growth of the links can be modeled by a non-homogeneous Poisson process with a constant rate across the day but varying rates from day to day, plus a nightly inactive period when local users are expected to be asleep. Conclusions are drawn based on analyzing four different datasets, three of which are regional and a non-regional one is included for contrast.

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