Object

Title: Detecting communities from networks: comparisonof algorithms on real and synthetic networks

Co-author(s) :

Mothe Josiane ; Haroutunian Mariam

Abstract:

Communities in real world complex networks correspond to hidden structures that are composed of nodes tightly connected among themselves and weakly connected with other nodes in the network. There are various applications of automatic community detection in computer science, medicine, machine learning, sociology, etc. In this paper, we first present the existing community detection algorithms and evaluation measures used in order to consider the algorithms effectiveness. We then report a deep comparison of the algorithms using both large scale real world complex networks and artificial networks generated from stochastic block model. We found that Louvain algorithm is consistently the best across both the measures and the networks (8 real world and many varied synthetic networks) we tested. Fast Greedy and Leading Eigenvector algorithms are also good alternatives. Moreover, compared to related work, our paper considers both more algorithms and more networks.

Publisher:

ITHEA

Date of publication:

2019

Identifier:

oai:noad.sci.am:135911

ISSN:

1310-0513

Language:

English

Journal or Publication Title:

Information Theories and Applications

Volume:

26

Number:

3

URL:


Additional Information:

karenmkhitaryan@gmail.com ; josiane.Mothe@irit.fr ; armar@ipia.sci.am

Affiliation:

Institute for Informatics and Automation Problems ; Institutde Recherche enInformatique de Toulouse

Country:

Armenia ; France

Object collections:

Last modified:

Mar 3, 2021

In our library since:

Jul 27, 2020

Number of object content hits:

25

All available object's versions:

https://noad.sci.am/publication/149504

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