Liu Qian1; He Xingkang2; Fang Haitao1
Source Publicationsciencechinainformationscience
AbstractSocial sampling is a novel randomized message passing protocol inspired by social communication for opinion formation in social networks. In a typical social sampling algorithm, each agent holds a sample from the empirical distribution of social opinions at initial time, and it collaborates with other agents in a distributed manner to estimate the initial empirical distribution by randomly sampling a message from current distribution estimate. In this paper, we focus on analyzing the theoretical properties of the distributed social sampling algorithm over random networks. First, we provide a framework based on stochastic approximation to study the asymptotic properties of the algorithm. Then, under mild conditions, we prove that the estimates of all agents converge to a common random distribution, which is composed of the initial empirical distribution and the accumulation of quantized error. Besides, by tuning algorithm parameters, we prove the strong consistency, namely, the distribution estimates of agents almost surely converge to the initial empirical distribution. Furthermore, the asymptotic normality of estimation error generated by distributed social sample algorithm is addressed. Finally, we provide a numerical simulation to validate the theoretical results of this paper.
Indexed ByCSCD
Citation statistics
Document Type期刊论文
Recommended Citation
GB/T 7714
Liu Qian,He Xingkang,Fang Haitao. asymptoticpropertiesofdistributedsocialsamplingalgorithm[J]. sciencechinainformationscience,2020,63(1).
APA Liu Qian,He Xingkang,&Fang Haitao.(2020).asymptoticpropertiesofdistributedsocialsamplingalgorithm.sciencechinainformationscience,63(1).
MLA Liu Qian,et al."asymptoticpropertiesofdistributedsocialsamplingalgorithm".sciencechinainformationscience 63.1(2020).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu Qian]'s Articles
[He Xingkang]'s Articles
[Fang Haitao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu Qian]'s Articles
[He Xingkang]'s Articles
[Fang Haitao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu Qian]'s Articles
[He Xingkang]'s Articles
[Fang Haitao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.