Change Point Detection in Dynamic Networks Based on Community Identification

Zhu, Tingting; Li, Ping; Yu, Lanlan; Chen, Kaiqi; Chen, Yan

doi:10.1109/tnse.2020.2973328

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…Note that detecting change-points in dynamic networks is a surging research area. More recent development include, in addition to the aforementioned references, Wang et al (2018);Zhu et al (2020a). Also note that the method of Zhao et al (2019) can be applied to detect multiple change-points for any dynamic networks.…”

Section: Modelsmentioning

confidence: 99%

“…Pensky (2019) studied the theoretical properties (such as the minimax lower bounds for the risk) of dynamic stochastic block model, assuming 'smooth' connectivity probabilities. The literature on change-point detection in dynamic networks include Yang et al (2011); Wang et al (2018); Wilson et al (2019); Zhao et al (2019); Bhattacharjee et al (2020); Zhu et al (2020a). While autoregressive models have been used in dynamic networks for modelling continuous responses observed from nodes (Zhu et al, 2017(Zhu et al, , 2019Chen et al, 2020;Zhu et al, 2020b), to our best knowledge no attempts have been made on modelling the dynamics of adjacency matrices in an autoregressive manner.…”

Section: Introductionmentioning

confidence: 99%

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Autoregressive Networks

Jiang¹,

Li²,

Yao³

2020

Preprint

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We propose a first-order autoregressive model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as the maximum likelihood estimators which are proved to be (uniformly) consistent and asymptotically normal. The model diagnostic checking can be carried out easily using a permutation test. The proposed model can apply to any Erdös-Renyi network processes with various underlying structures. As an illustration, an autoregressive stochastic block model has been investigated in depth, which characterizes the latent communities by the transition probabilities over time.This leads to a more effective spectral clustering algorithm for identifying the latent communities. Inference for a change-point is incorporated into the autoregressive stochastic block model to cater for possible structure changes. The developed asymptotic theory as well as the simulation study affirm the performance of the proposed methods. Application with three real data sets illustrates both relevance and usefulness of the proposed models.

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Section: Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autoregressive Networks

Jiang¹,

Li²,

Yao³

2020

Preprint

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“…Zhu e. al. approaches community detection based event detection differently [39]. Instead of detecting communities at each time interval, the authors consider each time interval as nodes of a larger network and detect communities in that large network.…”

Section: Related Workmentioning

confidence: 99%