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On the relation between Markov random fields and social networks

Bibliographic Data

ID7516793
AuthorsRoss Kindermann (University of Nebraska–Lincoln, corresponding author), Ross P Kindermann, J Laurie Snell (Dartmouth College)
Year1980
Volume7
Issue1
Pages1-13
Publication date1980-05-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Mathematical Sociology (JOURNAL)
Journal identifiersISSN: 0022-250X • E-ISSN: 1545-5874
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/0022250x.1980.9989895
OpenAlexW2032414074
LanguageEN
Citations received8
References cited10

Holland and Leinhardt (1977a) introduced a continuous time Markov chain to model changes in a social network. Their model was further studied by Wasserman (1977). Holland and Leinhardt (1977b) have also proposed a general class of probability measures on random networks. The purpose of this paper is to show that these probability measures may be viewed as Gibbs measures induced by a nearest neighbor potential. As such they have a Markov field property which is a natural generalization of Markov chains to spatial situations. The dynamic models are shown to fit into a general class of Markov processes suggested by the study of interacting particle systems. A related “voter model” is discussed

Class (philosophy · Discrete mathematics · Generalization · Markov chain · Markov model · Markov process · Markov property · Markov renewal process · Mathematical analysis · Physics · Random field · Statistical physics · Statistics · Variable-order Markov model · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Mathematics · Opinion Dynamics and Social Influence

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Unique citing works8
Citations per year0,18
Citation span1981 - 2021 (41)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 7

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