Proceedings ArticleDOI
Learning over social networks via diffusion adaptation
Xiaochuan Zhao,Ali H. Sayed +1 more
- pp 709-713
TLDR
It is shown that the diffusion algorithm converges almost surely to the true state and the superior convergence rate of the diffusion strategy over consensus-based strategies since diffusion schemes allow information to diffuse more thoroughly through the network.Abstract:
We propose a diffusion strategy to enable social learning over networks Individual agents observe signals influenced by the state of the environment The individual measurements are not sufficient to enable the agents to detect the true state of the environment on their own Agents are then encouraged to cooperate through a diffusive process of self-learning and social-learning We show that the diffusion algorithm converges almost surely to the true state Simulation results also illustrate the superior convergence rate of the diffusion strategy over consensus-based strategies since diffusion schemes allow information to diffuse more thoroughly through the networkread more
Citations
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Book
Adaptation, Learning, and Optimization Over Networks
TL;DR: The limits of performance of distributed solutions are examined and procedures that help bring forth their potential more fully are discussed and a useful statistical framework is adopted and performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks are derived.
Journal ArticleDOI
Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior
TL;DR: It is shown that it is an extraordinary property of biological networks that sophisticated behavior is able to emerge from simple interactions among lower-level agents.
Journal ArticleDOI
Social Learning Over Weakly Connected Graphs
TL;DR: It is shown that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations, and useful closed-form expressions are derived which can be used to motivate design problems to control it.
Journal ArticleDOI
Interplay Between Topology and Social Learning Over Weak Graphs
TL;DR: In this paper, the authors examine a distributed learning problem where the agents of a network form their beliefs about certain hypotheses of interest each agent collects streaming (private) data and updates continually its belief by means of a diffusion strategy, which blends the agent's data with the beliefs of its neighbors.
Proceedings ArticleDOI
Exponential Collapse of Social Beliefs over Weakly-connected Heterogeneous Networks
TL;DR: Analytical formulas are obtained that reveal how the agents’ detection capability and the network topology interplay to influence the asymptotic beliefs of the agents.
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