Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162567
Title: Indirect influence in social networks as an induced percolation phenomenon
Authors: Xie, Jiarong
Wang, Xiangrong
Feng, Ling
Zhao, Jin-Hua
Liu, Wenyuan
Moreno, Yamir
Hu, Yanqing
Keywords: Science::Physics
Issue Date: 2022
Source: Xie, J., Wang, X., Feng, L., Zhao, J., Liu, W., Moreno, Y. & Hu, Y. (2022). Indirect influence in social networks as an induced percolation phenomenon. Proceedings of the National Academy of Sciences of the United States of America, 119(9), 1-10. https://dx.doi.org/10.1073/pnas.2100151119
Journal: Proceedings of the National Academy of Sciences of the United States of America 
Abstract: Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.
URI: https://hdl.handle.net/10356/162567
ISSN: 0027-8424
DOI: 10.1073/pnas.2100151119
Rights: © 2022 The Authors. This article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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