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A framework for the construction of generative models for mesoscale structure in multilayer networks
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Bazzi, Marya, Jeub, Lucas G. S., Arenas, Alex, Howison, Sam D. and Porter, Mason A. (2020) A framework for the construction of generative models for mesoscale structure in multilayer networks. Physical Review Research, 2 . 023100. doi:10.1103/PhysRevResearch.2.023100 ISSN 2643-1564.
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Official URL: http://dx.doi.org/10.1103/PhysRevResearch.2.023100
Abstract
Multilayer networks allow one to represent diverse and coupled connectivity patterns—such as 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, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks.
| Item Type: | Journal Article | |||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Computer networks, Computational complexity, System analysis, Information networks, Social sciences -- Network analysis | |||||||||||||||||||||
| Journal or Publication Title: | Physical Review Research | |||||||||||||||||||||
| Publisher: | American Physical Society | |||||||||||||||||||||
| ISSN: | 2643-1564 | |||||||||||||||||||||
| Official Date: | 30 April 2020 | |||||||||||||||||||||
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| Volume: | 2 | |||||||||||||||||||||
| Article Number: | 023100 | |||||||||||||||||||||
| DOI: | 10.1103/PhysRevResearch.2.023100 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
| Copyright Holders: | © 2020 American Physical Society | |||||||||||||||||||||
| Date of first compliant deposit: | 24 April 2020 | |||||||||||||||||||||
| Date of first compliant Open Access: | 13 May 2020 | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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