Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel generic model of opinion dynamics over a social network, in the presence of communication among the users leading to interpersonal influence i.e., peer pressure. Each individual in the social network has a distinct objective function representing a weighted sum of internal and external pressures. We prove conditions under which a connected group of users converges to a fixed opinion distribution, and under which conditions the group reaches consensus. Through simulation, we study the rate of convergence on large scale-free networks as well as the impact of user stubbornness on convergence in a simple political model. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Social and Information Networks | Convergence (routing),Gradient descent,Social network,Computer science,Peer pressure,Artificial intelligence,Interpersonal influence,Rate of convergence,Connectivity,Weighted arithmetic mean,Machine learning |
DocType | Volume | Citations |
Journal | abs/1702.07912 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Semonsen | 1 | 6 | 1.45 |
| Christopher Griffin | 2 | 58 | 11.43 |
| Anna Cinzia Squicciarini | 3 | 1301 | 106.30 |
| Sarah Michele Rajtmajer | 4 | 31 | 10.06 |