Generative Benchmark Models for Mesoscale Structure in Multilayer Networks. Bazzi, M., Jeub, L. G. S., Arenas, A., Howison, S. D., & Porter, M. A. ![Generative Benchmark Models for Mesoscale Structure in Multilayer Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Multilayer networks allow one to represent diverse and interdependent connectivity patterns –- e.g., time-dependence, multiple subsystems, or both –- that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate "mesoscale" (i.e., intermediate-scale) structures, such as dense sets of nodes known as "communities" that are connected sparsely to each other, to discover network features that are not apparent at the microscale or the macroscale. A variety of methods and algorithms are available to identify communities in multilayer networks, but they differ in their definitions and/or assumptions of what constitutes a community, and many scalable algorithms provide approximate solutions with little or no theoretical guarantee on the quality of their approximations. Consequently, it is crucial to develop generative models of networks to use as a common test of community-detection tools. In the present paper, we develop a family of benchmarks for detecting mesoscale structures in multilayer networks by introducing a generative model that can explicitly incorporate dependency structure between layers. Our benchmark provides a standardized set of null models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. We discuss the parameters and properties of our generative model, and we illustrate its use by comparing a variety of community-detection methods.
@article{bazziGenerativeBenchmarkModels2016,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1608.06196},
primaryClass = {cond-mat, physics:nlin, physics:physics, stat},
title = {Generative {{Benchmark Models}} for {{Mesoscale Structure}} in {{Multilayer Networks}}},
url = {http://arxiv.org/abs/1608.06196},
abstract = {Multilayer networks allow one to represent diverse and interdependent connectivity patterns --- e.g., time-dependence, multiple subsystems, or both --- that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate "mesoscale" (i.e., intermediate-scale) structures, such as dense sets of nodes known as "communities" that are connected sparsely to each other, to discover network features that are not apparent at the microscale or the macroscale. A variety of methods and algorithms are available to identify communities in multilayer networks, but they differ in their definitions and/or assumptions of what constitutes a community, and many scalable algorithms provide approximate solutions with little or no theoretical guarantee on the quality of their approximations. Consequently, it is crucial to develop generative models of networks to use as a common test of community-detection tools. In the present paper, we develop a family of benchmarks for detecting mesoscale structures in multilayer networks by introducing a generative model that can explicitly incorporate dependency structure between layers. Our benchmark provides a standardized set of null models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. We discuss the parameters and properties of our generative model, and we illustrate its use by comparing a variety of community-detection methods.},
urldate = {2018-04-30},
date = {2016-08-22},
keywords = {Physics - Physics and Society,Statistics - Methodology,Computer Science - Social and Information Networks,Condensed Matter - Statistical Mechanics,Nonlinear Sciences - Adaptation and Self-Organizing Systems},
author = {Bazzi, Marya and Jeub, Lucas G. S. and Arenas, Alex and Howison, Sam D. and Porter, Mason A.},
file = {/home/dimitri/Nextcloud/Zotero/storage/LRM9HWTC/Bazzi et al. - 2016 - Generative Benchmark Models for Mesoscale Structur.pdf;/home/dimitri/Nextcloud/Zotero/storage/JM7VWEGD/1608.html}
}
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