ITU

Community Detection in Multiplex Networks

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

Standard

Community Detection in Multiplex Networks. / Magnani, Matteo; Hanteer, Obaida; Interdonato, Roberto; Rossi, Luca; Tagarelli, Andrea.

In: ACM Computing Surveys, Vol. 54, No. 3, 48, 05.2021.

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

Harvard

APA

Vancouver

Author

Magnani, Matteo ; Hanteer, Obaida ; Interdonato, Roberto ; Rossi, Luca ; Tagarelli, Andrea. / Community Detection in Multiplex Networks. In: ACM Computing Surveys. 2021 ; Vol. 54, No. 3.

Bibtex

@article{e5b4d3b948a34de39cfd0bcbd9cda9dd,
title = "Community Detection in Multiplex Networks",
abstract = "A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.",
author = "Matteo Magnani and Obaida Hanteer and Roberto Interdonato and Luca Rossi and Andrea Tagarelli",
year = "2021",
month = may,
doi = "10.1145/3444688",
language = "English",
volume = "54",
journal = "A C M Computing Surveys",
issn = "0360-0300",
publisher = "Association for Computing Machinery",
number = "3",

}

RIS

TY - JOUR

T1 - Community Detection in Multiplex Networks

AU - Magnani, Matteo

AU - Hanteer, Obaida

AU - Interdonato, Roberto

AU - Rossi, Luca

AU - Tagarelli, Andrea

PY - 2021/5

Y1 - 2021/5

N2 - A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

AB - A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

U2 - 10.1145/3444688

DO - 10.1145/3444688

M3 - Journal article

VL - 54

JO - A C M Computing Surveys

JF - A C M Computing Surveys

SN - 0360-0300

IS - 3

M1 - 48

ER -

ID: 85950593