期刊
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
卷 3, 期 2, 页码 222-238出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSIPN.2017.2668138
关键词
Bayesian update; diffusion strategy; leader-follower relationship; social learning; weakly-connected networks.
资金
- National Science Foundation [CCF-1524250, ECCS-1407712]
- DARPA [N66001-14-2-4029]
- Visiting Professorship from the Leverhulme Trust, U.K
- Direct For Computer & Info Scie & Enginr [1524250] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [1524250] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1407712] Funding Source: National Science Foundation
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 paper, a scenario of total influence (or mind-control) arises where a set of influential agents ends up shaping the beliefs of noninfluential 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.
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