Title: Unsupervised graph clustering for community detection in complex networks using spectral analysis

Authors: Zakariyaa Ait El Mouden; Abdeslam Jakimi; Moha Hajar

Addresses: Departments of Computer Science and Department of Mathematics, Faculty of Sciences and Techniques Errachidia, Moulay Ismail University, Meknes, 50000, Morocco ' Departments of Computer Science and Department of Mathematics, Faculty of Sciences and Techniques Errachidia, Moulay Ismail University, Meknes, 50000, Morocco ' Departments of Computer Science and Department of Mathematics, Faculty of Sciences and Techniques Errachidia, Moulay Ismail University, Meknes, 50000, Morocco

Abstract: Clustering is a recent technique for a smart classification of data, where the output is a set of clusters and each cluster regroups data points having similar behaviours. Traditional clustering algorithms are those where we predefine the number of clusters as an input parameter, and we control the size of the neighbourhood, also called supervised clustering. Recently, with data evolution in term of volume and variety, supervised clustering techniques were overwhelmed and unsupervised algorithms started to appear. Spectral clustering (SC) is a graph clustering technique based on spectral analysis, and it is one of the most powerful unsupervised clustering techniques. This paper presents an application of a spectral clustering algorithm to data modelled by graphs and a comparison between the two families of SC; unnormalised SC and normalised SC. We also introduce a modification of normalised SC algorithm to make the number of clusters estimated and not given as an input parameter. Further works are needed to apply the approach to larger datasets in order to evaluate its performance against big data challenges.

Keywords: big data; graph clustering; spectral clustering; community detection; complex networks; spectral analysis.

DOI: 10.1504/IJMIS.2020.114775

International Journal of Multimedia Intelligence and Security, 2020 Vol.3 No.4, pp.337 - 347

Received: 06 Sep 2019
Accepted: 23 Feb 2020

Published online: 16 Apr 2021 *

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