NDLI: Tracking a Markov-Modulated Stationary Degree Distribution of a Dynamic Random Graph

Content Provider	IEEE Xplore Digital Library
Author	Hamdi, M. Krishnamurthy, V. Yin, G.
Copyright Year	1963
Abstract	This paper considers a Markov-modulated duplication-deletion random graph where at each time instant, one node can either join or leave the network; the probabilities of joining or leaving evolve according to the realization of a finite state Markov chain. Two results are presented. First, motivated by social network applications, the asymptotic behavior of the degree distribution is analyzed. Second, a stochastic approximation algorithm is presented to track empirical degree distribution as it evolves over time. The tracking performance of the algorithm is analyzed in terms of mean square error and a functional central limit theorem is presented for the asymptotic tracking error. Also, a Hilbert-space-valued stochastic approximation algorithm that tracks a Markov-modulated probability mass function with support on the set of nonnegative integers is analyzed.
Sponsorship	IEEE Information Theory Society
Starting Page	6609
Ending Page	6625
Page Count	17
File Size	754532
File Format	PDF
ISSN	00189448
Volume Number	60
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov processes Approximation algorithms Approximation methods Heuristic algorithms Social network services Algorithm design and analysis stochastic approximation algorithms Adaptive algorithms complex networks degree distribution Markov-modulated random graphs power law social networks
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems Computer Science Applications

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