Information diffusion in signed networks

Xiaochen He; Haifeng Du; Marcus W Feldman; Guangyu Li

doi:10.1371/journal.pone.0224177

Information diffusion in signed networks

PLoS One. 2019 Oct 29;14(10):e0224177. doi: 10.1371/journal.pone.0224177. eCollection 2019.

Authors

Xiaochen He^{1

2}, Haifeng Du¹, Marcus W Feldman^{1

3}, Guangyu Li¹

Affiliations

¹ Center for Administration and Complexity Science, Xi'an Jiaotong University, Xi'an, China.
² Department of Sociology, Cornell University, Ithaca, New York, United States of America.
³ Department of Biology, Stanford University, Stanford, California, United States of America.

Abstract

Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of information based on balance theory, and find that a signed network can generate path dependence while structural balance can help remove the path dependence when seeded with balanced initialized active nodes. Simulation shows that the diffusion of information based on positive links contradicts that based on negative links. More positive links in signed networks are more likely to activate nodes and remove path dependence, but they can reduce predictability that is based on active states. We also find that a balanced structure can facilitate both the magnitude and speed of information diffusion, remove the path dependence, and cause polarization.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computer Simulation*
Diffusion
Humans
Interpersonal Relations
Models, Psychological
Models, Theoretical*
Social Support*

Grants and funding

This research was supported by the Major Project of the National Social Science Foundation of China (Grant No: 15ZDA048), http://www.npopss-cn.gov.cn/ (XH, HD, GL); The Humanities and Social Science Talent Plan, http://www.snedu.gov.cn/ (XH, HD, GL); and the Morrison Institute for Population and Resource Studies at Stanford University (MWF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.