Discovering Communities in Networks: A Linear Programming Approach Using Max-Min Modularity

Ferdowsi, Arman; Khanteymoori, Alireza

doi:10.15439/2021F65

Artykuł - szczegóły

Tytuł artykułu

Discovering Communities in Networks: A Linear Programming Approach Using Max-Min Modularity

Autorzy

Ferdowsi Arman , Khanteymoori Alireza

Wybrane pełne teksty z tego czasopisma

http://annals-csis.org

Identyfikatory

DOI

10.15439/2021F65

Warianty tytułu

Konferencja

Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)

Języki publikacji

Abstrakty

Community detection is a fundamental challenge in network science and graph theory that aims to reveal nodes' structures. ‎While most methods consider Modularity as a community quality measure‎, ‎Max-Min Modularity improves the accuracy of the measure by penalizing the Modularity quantity when unrelated nodes are in the same community‎. ‎In this paper‎, ‎we propose a community detection approach based on linear programming using Max-Min Modularity‎. ‎The experimental results show that our algorithm has a better performance than the previously known algorithms on some well-known instances‎.

Słowa kluczowe

complex networks graph theory linear programming social networking

złożone sieci teoria grafów Programowanie liniowe sieć społecznościowa

Wydawca

Polskie Towarzystwo Informatyczne

Czasopismo

Annals of Computer Science and Information Systems

Rocznik

2021

Tom

Vol. 25

Strony

329–--335

Opis fizyczny

Bibliogr. 51 poz., wz., wykr.

Twórcy

autor

Ferdowsi Arman

aferdowsi@ecs.tuwien.ac.at

Vienna University of Technology Institute of Computer Engineering Embedded Computing Systems

autor

Khanteymoori Alireza

alireza@informatik.uni-freiburg.de

University of Freiburg Department of Computer Science Bioinformatics Group

Bibliografia

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Uwagi

Track 3: Network Systems and Applications

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-8e530eea-d928-4a95-bec7-cb2658af8bf9