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Modified Design Approach Using Cuckoo Search Optimization Algorithm for Mitigation of Harmonics and Improvement of Efficiency in Back Light LED TV Power Supply Read chapter Read first chapter

2020 | OriginalPaper | Chapter

A Comparative Analysis of Community Detection Methods in Massive Datasets

Authors : B. S. A. S. Rajita, Deepa Kumari, Subhrakanta Panda

Published in: Modelling, Simulation and Intelligent Computing

Publisher: Springer Singapore

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Abstract

Nowadays there is a boom in social network data streaming from various fields of interest related to finance, engineering, medicine, and general sciences. All these data are modeled as graphs for better analysis. Community detection is one such mechanism for the analysis of such massive data. Many community detection algorithms exist in literature. The existing algorithms are compared by using either real-world or artificial networks (modeled as graphs) but not both. This paper aims to make a comparative study of two popular existing community detection algorithms both on real-world and synthetic data and verify their performance. The approach in this paper makes good use of recent advances in graphical modeling of different social networks. We generated a random graph that represents most of the observed properties of a real-world dataset. The experimental results are tabulated and the computed metrics help in inferring the suitability or scalability of an algorithm for small or massive datasets.

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Metadata
Title
A Comparative Analysis of Community Detection Methods in Massive Datasets
Authors
B. S. A. S. Rajita
Deepa Kumari
Subhrakanta Panda
Copyright Year
2020
Publisher
Springer Singapore
Book
Modelling, Simulation and Intelligent Computing

Print ISBN: 978-981-15-4774-4

Electronic ISBN: 978-981-15-4775-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-4775-1_19

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