Open AccessIndirect influence in social networks as an induced percolation phenomenon
Author(s) -
Jiarong Xie,
Xiangrong Wang,
Ling Feng,
Jin-Hua Zhao,
Wenyuan Liu,
Yamir Moreno,
Yanqing Hu
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2100151119
Subject(s) - percolation (cognitive psychology) , statistical physics , mechanism (biology) , phenomenon , indirect effect , empirical research , computer science , physics , psychology , mathematics , neuroscience , political science , quantum mechanics , statistics , law
Significance Increasing empirical evidence in diverse social and ecological systems has shown that indirect interactions play a pivotal role in shaping systems’ dynamical behavior. Our empirical study on collaboration networks of scientists further reveals that an indirect effect can dominate over direct influence in behavioral spreading. However, almost all models in existence focus on direct interactions, and the general impact of indirect interactions has not been studied. We propose a new percolation process, termed induced percolation, to characterize indirect interactions and find that indirect interactions raise a plethora of new phenomena, including the wide range of possible phase transitions. Such an indirect mechanism leads to very different spreading outcomes from that of direct influences.