Dynamic stochastic block models:Parameter estimation and detection of changes in community structure

Ludkin, Matthew and Neal, Peter John and Eckley, Idris Arthur (2018) Dynamic stochastic block models:Parameter estimation and detection of changes in community structure. Statistics and Computing, 28 (6). pp. 1201-1213. ISSN 0960-3174

[img]
Preview
PDF (STCO_paper_v3)
STCO_paper_v3.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (364kB)

Abstract

The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership. In this paper we introduce an autoregressive extension of the SBM. This is based on continuous time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9788-9
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
88466
Deposited By:
Deposited On:
30 Oct 2017 10:58
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Jul 2020 06:53