Benchmark model to assess community structure in evolving networks

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Benchmark model to assess community structure in evolving networks. / Granell, Clara; Darst, Richard K; Arenas, Alex; Fortunato, Santo; Gómez, Sergio.

In: Physical Review E, Vol. 92, No. 1, 012805, 2015, p. 1-8.

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Granell, C, Darst, RK, Arenas, A, Fortunato, S & Gómez, S 2015, 'Benchmark model to assess community structure in evolving networks', Physical Review E, vol. 92, no. 1, 012805, pp. 1-8. https://doi.org/10.1103/PhysRevE.92.012805

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Granell, Clara ; Darst, Richard K ; Arenas, Alex ; Fortunato, Santo ; Gómez, Sergio. / Benchmark model to assess community structure in evolving networks. In: Physical Review E. 2015 ; Vol. 92, No. 1. pp. 1-8.

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@article{dda81d766220478e98a4a0ddbbc79b41,
title = "Benchmark model to assess community structure in evolving networks",
abstract = "Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario.",
author = "Clara Granell and Darst, {Richard K} and Alex Arenas and Santo Fortunato and Sergio G{\'o}mez",
note = "VK: Fortunato, S.; Multiplex; TRITON",
year = "2015",
doi = "10.1103/PhysRevE.92.012805",
language = "English",
volume = "92",
pages = "1--8",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "1",

}

RIS - Download

TY - JOUR

T1 - Benchmark model to assess community structure in evolving networks

AU - Granell, Clara

AU - Darst, Richard K

AU - Arenas, Alex

AU - Fortunato, Santo

AU - Gómez, Sergio

N1 - VK: Fortunato, S.; Multiplex; TRITON

PY - 2015

Y1 - 2015

N2 - Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario.

AB - Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario.

UR - http://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.012805

U2 - 10.1103/PhysRevE.92.012805

DO - 10.1103/PhysRevE.92.012805

M3 - Article

VL - 92

SP - 1

EP - 8

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 1

M1 - 012805

ER -

ID: 2024733