Name
Abstract | ||
|---|---|---|
In this paper, we study a model of agent consensus in a social network in the presence increasing interagent influence, i.e., increasing peer pressure. Each agent in the social network has a distinct social stress function given by a weighted sum of internal and external behavioral pressures. We assume a weighted average update rule consistent with the classic DeGroot model and prove conditions under which a connected group of agents converge to a fixed opinion distribution, and under which conditions the group reaches consensus. We show that the update rule converges to gradient descent and explain its transient and asymptotic convergence properties. Through simulation, we study the rate of convergence on a scale-free network. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCYB.2018.2799858 | IEEE transactions on cybernetics |
Keywords | Field | DocType |
Convergence,Social network services,Cybernetics,Eigenvalues and eigenfunctions,Stress,Resistance,Laplace equations | Convergence (routing),Mathematical optimization,Gradient descent,Social network,Peer pressure,Rate of convergence,Opinion dynamics,Mathematics,Weighted arithmetic mean,Cybernetics | Journal |
Volume | Issue | ISSN |
49 | 4 | 2168-2275 |
Citations | PageRank | References |
3 | 0.38 | 23 |
Authors | ||
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 Rajtmajer | 4 | 4 | 1.74 |