Lightweight Support Vector Clustering Algorithm for Community Detection in Complex Networks

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Abstract

The community structure is one of the most attractive properties of a complex network. This structure has been fundamental to advancements in various scientific branches. Numerous tools that involve community detection algorithms have been used in recent studies. In this paper, we propose a lightweight support vector clustering method. It surpasses traditional support vector approaches in terms of accuracy and complexity on account of its innovative design of distance calculations and the utilization of stable equilibrium points in the community assignment process. Extensive experiments are undertaken in computer-generated networks as well as real-world datasets. The results illustrate the competitive performance of the proposed algorithm compared to its community detection counterparts.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages2317-2322
Number of pages6
ISBN (Electronic)9789881563941
DOIs
Publication statusPublished - 5 Oct 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Community Detection
  • Complex Network
  • Machine Learning
  • Optimization
  • Support Vector Clustering

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