asymptoticpropertiesofdistributedsocialsamplingalgorithm

	asymptoticpropertiesofdistributedsocialsamplingalgorithm
	Liu Qian 1; He Xingkang 2; Fang Haitao 1
刊名	sciencechinainformationscience
	2020
卷号	63 期号:1
ISSN号	1674-733X
英文摘要	Social 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.
语种	英语
CSCD记录号	CSCD:6655421
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/51274]
专题	中国科学院数学与系统科学研究院
作者单位	1.中国科学院数学与系统科学研究院 2.皇家工学院
推荐引用方式 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).