UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Disentangling group and link persistence in dynamic stochastic block models

Barucca, P; Lillo, F; Mazzarisi, P; Tantari, D; (2018) Disentangling group and link persistence in dynamic stochastic block models. Journal of Statistical Mechanics: Theory and Experiment , 2018 , Article 123407. 10.1088/1742-5468/aaeb44. Green open access

[thumbnail of BLMT_JSTAT_final.pdf]
Preview
Text
BLMT_JSTAT_final.pdf - Accepted version

Download (1MB) | Preview

Abstract

We study the inference of a model of dynamic networks in which both communities and links keep memory of previous network states. By considering maximum likelihood inference from single snapshot observations of the network, we show that link persistence makes the inference of communities harder, decreasing the detectability threshold, while community persistence tends to make it easier. We analytically show that communities inferred from single network snapshot can share a maximum overlap with the underlying communities of a specific previous instant in time. This leads to time-lagged inference: the identification of past communities rather than present ones. Finally we compute the time lag and propose a corrected algorithm, the lagged snapshot dynamic algorithm, for community detection in dynamic networks. We analytically and numerically characterize the detectability transitions of such algorithm as a function of the memory parameters of the model and we make a comparison with a full dynamic inference.

Type: Article
Title: Disentangling group and link persistence in dynamic stochastic block models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1742-5468/aaeb44
Publisher version: https://doi.org/10.1088/1742-5468/aaeb44
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10070929
Downloads since deposit
26Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item