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Detecting outbreaks in temporally dependent networks
Author(s) -
HazratiMarangaloo Hossein,
Noorossana Rassoul
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2473
Subject(s) - generalization , computer science , dependency (uml) , representation (politics) , chart , likelihood ratio test , graph , multivariate statistics , dynamic network analysis , path (computing) , network model , enhanced data rates for gsm evolution , data mining , algorithm , statistics , artificial intelligence , mathematics , machine learning , theoretical computer science , mathematical analysis , computer network , politics , political science , law , programming language
Dynamic networks require effective methods of monitoring and surveillance in order to respond promptly to unusual disturbances. In many applications, it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. In this paper, a dynamic random graph model is proposed that takes into account the past activities of the individuals in the social network and also represents temporal dependency of the network. The model parameters are appearance and disappearance probabilities of an edge which are estimated using a maximum likelihood approach. A generalization of a single path‐dependent likelihood ratio test is employed to detect changes in the parameters of the proposed model. Through monitoring the estimated parameters, one can effectively detect structural changes in a temporal‐dependent network. The proposed model is employed to describe the behavior of a real network, and its parameters are monitored via dependent likelihood ratio test and multivariate exponentially weighted moving average control chart. Results indicate that the proposed dynamic random graph model is a reliable mean to modeling and detecting changes in temporally dependent networks.