Comparative Analysis of Classic Clustering Algorithms and Girvan-Newman Algorithm for Finding Communities in Social Networks

8 Pages Posted: 20 Nov 2018

See all articles by Jelena Ljucović

Jelena Ljucović

University Mediterranean

Tijana Vujičić

University Mediterranean

Tripo Matijević

University Mediterranean

Savo Tomović

University Mediterranean

Snežana Šćepanović

University Mediterranean, Faculty for Information Technology

Date Written: September 8, 2016

Abstract

Nowadays finding patterns in large social network datasets is a growing challenge and an important subject of interest. One of current problems in this field is identifying clusters within social networks with large number of nodes. Social network clusters are not necessarily disjoint sets; rather they may overlap and have common nodes, in which case it is more appropriate to designate them as communities. Although many clustering algorithms handle small datasets well, they are usually extremely inefficient on large datasets. This paper shows comparative analysis of frequently used classic graph clustering algorithms and well-known Girvan-Newman algorithm that is used for identification of communities in graphs, which is especially optimized for large datasets. The goal of the paper is to show which of the algorithms give best performances on given dataset. The paper presents real problem of data clustering, algorithms that can be used for its solution, methodology of analysis, results that were achieved and conclusions that were derived.

Keywords: data mining, datasets, clusters, communities, graphs, social networks, ICT, Girvan-Newman algorithm, clustering algorithms

JEL Classification: C8

Suggested Citation

Ljucović, Jelena and Vujičić, Tijana and Matijević, Tripo and Tomović, Savo and Šćepanović, Snežana, Comparative Analysis of Classic Clustering Algorithms and Girvan-Newman Algorithm for Finding Communities in Social Networks (September 8, 2016). 2016 ENTRENOVA Conference Proceedings. Available at SSRN: https://ssrn.com/abstract=3282270

Jelena Ljucović (Contact Author)

University Mediterranean ( email )

Podgorica
Montenegro

Tijana Vujičić

University Mediterranean ( email )

Podgorica
Montenegro

Tripo Matijević

University Mediterranean ( email )

Podgorica
Montenegro

Savo Tomović

University Mediterranean ( email )

Podgorica
Montenegro

Snežana Šćepanović

University Mediterranean, Faculty for Information Technology ( email )

Montenegro

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