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Detection and localization of change points in temporal networks with the aid of stochastic block models

Simon De Ridder UGent, Benjamin Vandermarliere UGent and Jan Ryckebusch UGent (2016) JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT.
abstract
A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Stochastic Block models, Belief Propagation, Network analysis, Change point detection
journal title
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
J. Stat. Mech.-Theory Exp.
article number
113302
pages
18 pages
Web of Science type
Article
Web of Science id
000388261000002
JCR category
PHYSICS, MATHEMATICAL
JCR impact factor
2.196 (2016)
JCR rank
8/55 (2016)
JCR quartile
1 (2016)
ISSN
1742-5468
DOI
10.1088/1742-5468/2016/11/113302
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8157388
handle
http://hdl.handle.net/1854/LU-8157388
date created
2016-11-17 11:12:00
date last changed
2017-11-16 14:36:27
@article{8157388,
  abstract     = {A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.},
  articleno    = {113302},
  author       = {De Ridder, Simon and Vandermarliere, Benjamin and Ryckebusch, Jan},
  issn         = {1742-5468},
  journal      = {JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT},
  keyword      = {Stochastic Block models,Belief Propagation,Network analysis,Change point detection},
  language     = {eng},
  pages        = {18},
  title        = {Detection and localization of change points in temporal networks with the aid of stochastic block models},
  url          = {http://dx.doi.org/10.1088/1742-5468/2016/11/113302},
  year         = {2016},
}

Chicago
De Ridder, Simon, Benjamin Vandermarliere, and Jan Ryckebusch. 2016. “Detection and Localization of Change Points in Temporal Networks with the Aid of Stochastic Block Models.” Journal of Statistical Mechanics-theory and Experiment.
APA
De Ridder, Simon, Vandermarliere, B., & Ryckebusch, J. (2016). Detection and localization of change points in temporal networks with the aid of stochastic block models. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT.
Vancouver
1.
De Ridder S, Vandermarliere B, Ryckebusch J. Detection and localization of change points in temporal networks with the aid of stochastic block models. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT. 2016;
MLA
De Ridder, Simon, Benjamin Vandermarliere, and Jan Ryckebusch. “Detection and Localization of Change Points in Temporal Networks with the Aid of Stochastic Block Models.” JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2016): n. pag. Print.