Name
Title | ||
|---|---|---|
The Volatility of Weak Ties: Co-evolution of Selection and Influence in Social Networks |
Abstract | ||
|---|---|---|
In this work we look at opinion formation and the effects of two phenomena both of which promote consensus between agents connected by ties: influence, agents changing their opinions to match their neighbors; and selection, agents re-wiring to connect to new agents when the existing neighbor has a different opinion. In our agent-based model, we assume that only weak ties can be rewired and strong ties do not change. The network structure as well as the opinion landscape thus co-evolve with two important parameters: the probability of influence versus selection; and the fraction of strong ties versus weak ties. Using empirical and theoretical methodologies we discovered that on a two-dimensional spatial network: With no/low selection the presence of weak ties enables fast consensus. This conforms with the classical theory that weak ties are helpful for quickly mixing and spreading information, and strong ties alone act much more slowly. With high selection, too many weak ties inhibit any consensus at all-the graph partitions. The weak ties reinforce the differing opinions rather than mixing them. However, sufficiently many strong ties promote convergence, though at a slower pace. We additionally test the aforementioned results using a real network. Our study relates two theoretical ideas: the strength of weak tiesthat weak ties are useful for spreading information; and the idea of echo chambers or filter bubbles, that people are typically bombarded by the opinions of like-minded individuals. The difference is in how (much) selection operates. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3331748 | adaptive agents and multi-agents systems |
Keywords | Field | DocType |
Opinion formation,Polarization,Echo chambers,Tie strength,Social network structure | Convergence (routing),Graph,Pace,Social network,Opinion formation,Spatial network,Computer science,Microeconomics,Artificial intelligence,Volatility (finance),Machine learning,Interpersonal ties | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Gao | 1 | 2174 | 155.61 |
| Grant Schoenebeck | 2 | 509 | 39.48 |
| Fang-Yi Yu | 3 | 4 | 6.57 |