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2019 Spectral clustering in the dynamic stochastic block model
Marianna Pensky, Teng Zhang
Electron. J. Statist. 13(1): 678-709 (2019). DOI: 10.1214/19-EJS1533


In the present paper, we have studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most $s$ nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering. The procedure is computationally viable, adaptive to the unknown smoothness of the functional connection probabilities, to the rate $s$ of membership switching, and to the unknown number of clusters. In addition, it is accompanied by non-asymptotic guarantees for the precision of estimation and clustering.


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Marianna Pensky. Teng Zhang. "Spectral clustering in the dynamic stochastic block model." Electron. J. Statist. 13 (1) 678 - 709, 2019.


Received: 1 March 2018; Published: 2019
First available in Project Euclid: 16 February 2019

zbMATH: 07038001
MathSciNet: MR3914178
Digital Object Identifier: 10.1214/19-EJS1533

Primary: 05C80 , 62F12
Secondary: 62H30

Keywords: adaptive estimation , dynamic stochastic block model , spectral clustering , Time-varying network


Vol.13 • No. 1 • 2019
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