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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Optimal timescale for community detection in growing networks
Organization Unit
Authors
  • Matus Medo
  • An Zeng
  • Yi-Cheng Zhang
  • Manuel Mariani
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
Language
  • English
Journal Title New Journal of Physics
Publisher IOP Publishing
Geographical Reach international
ISSN 1367-2630
Page Range Epub ahead of print
Date 2019
Abstract Text Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network's nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them.
Free access at DOI
Digital Object Identifier 10.1088/1367-2630/ab413f
Other Identification Number merlin-id:18212
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