The Evolution of Beliefs over Signed Social Networks

• Published in: Operations research Vol. 64; no. 3; pp. 585 - 604
• Main Authors: Shi, Guodong, Proutiere, Alexandre, Johansson, Mikael, Baras, John S., Johansson, Karl H.
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01-05-2016; Institute for Operations Research and the Management Sciences
• Subjects: Analysis; Asymptotic methods; Clustering; Convergence; Evolution; Graph theory; Links; Mathematical models; Nodes; Operations research; opinion clustering; opinion dynamics; Phase transitions; signed graph; Social networks; Theorems; Iran
• ISSN: 0030-364X; 1526-5463; 1526-5463
• DOI: 10.1287/opre.2015.1448

We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes the DeGroot model to account for negative links: when two adversaries interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a
no-survivor
theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a
live-or-die
lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon.