Social Learning over Weakly-Connected Graphs
Social and Information Networks
2017-01-10 v2 Multiagent Systems
Abstract
In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this work, a scenario of total influence (or "mind-control") arises where a set of influential agents ends up shaping the beliefs of non-influential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.
Cite
@article{arxiv.1609.03703,
title = {Social Learning over Weakly-Connected Graphs},
author = {Hawraa Salami and Bicheng Ying and Ali H. Sayed},
journal= {arXiv preprint arXiv:1609.03703},
year = {2017}
}
Comments
To appear in 2017 in the IEEE Transactions on Signal and Information Processing over Networks