Object

Title: Community Detection: Comparison of State of the ArtAlgorithms

Co-author(s) :

Mkhitaryan Karen ; Haroutunian Mariam

Abstract:

Real world complex networks may contain hidden structures called communities or groups. They are composed of nodes being tightly connected within those groups and weakly connected between them. Detecting communities has numerous applications in different sciences such as biology, social network analysis, economics and computer science. Since there is no universally accepted definition of community, it is a complicated task to distinguish community detection algorithms as each of them use a different approach, resulting in different outcomes. Thus large number of articles are devoted to investigating community detection algorithms, implementation on both real world and artificial data sets and development of evaluation measures. In this article several state of the art algorithms and evaluation measures are studied which are used in clustering and community detection literature. The main focus of this article is to survey recent work and evaluate them using artificially generated networks.

Identifier:

oai:noad.sci.am:135806

Language:

English

URL:


Additional Information:

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

Affiliation:

Univ. de Toulouse ; IRIT, UMR5505 CNRS& ESPE ; Institute for Informatics and Automation Problems

Country:

Armenia

Year:

2017

Time period:

September25-29

Conference title:

11th International Conference on Computer Science and Information Technologies CSIT 2017

Place:

Yerevan

Participation type:

oral

Object collections:

Last modified:

Mar 3, 2021

In our library since:

Jul 17, 2020

Number of object content hits:

5

All available object's versions:

https://noad.sci.am/publication/149333

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

This page uses 'cookies'. More information