Discovering community structure in Complex Network through Community Detection Approach

被引：0

作者：

Ismail, Suriana ^{[1
]}

Ismail, Roslan ^{[1
]}

机构：

[1] Univ Kuala Lumpur, Malaysian Inst Informat Technol, Jalan Sultan Ismail, Kuala Lumpur, Malaysia

来源：

PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2018) | 2018年

关键词：

Complex networks; Biological network; Modularity;

D O I：

10.1145/3164541.3164599

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Complex network analysis which can be represented as graph has gained much interest from researchers recently. Analysis derived from complex network leading to a discovery of important group or community lies within the network. It imposes a significant challenge to computer scientists, physicists, and sociologists alike, to identify and discover the true meaning of community for complex network. Different community detection algorithms have been proposed in different perspective of almost similar aim of identifying the community. In this paper, we apply the modularity measurement on complex network and test the strengthness of community found by algorithm proposed. The main study focuses on the importance of having robust algorithm in detecting communities in different type of complex network. Experimental results show that the method is able to successfully separate community by achieving an ideal modularity value.

引用

页数：4

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