Համահեղինակ(ներ):

Mkhitaryan Karen ; Haroutunian Mariam

Ամփոփում:

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.

Նույնականացուցիչ:

oai:noad.sci.am:135806

Լեզու:

English

URL:

լրացուցիչ տեղեկատվություն:

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

Կազմակերպության անվանում:

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

Երկիր:

Armenia

Տարի:

2017

Ժամանակահատված:

September25-29

Գիտաժողովի անվանում:

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

Վայր:

Yerevan

Մասնակցության տեսակը:

oral