Application Areas of Community Detection: A Review

In the realm of today's real world, information systems are represented by complex networks. Complex networks contain a community structure inherently. Community is a set of members strongly connected within members and loosely connected with the rest of the network. Community detection is the...

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Bibliographic Details
Published in2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) pp. 65 - 70
Main Authors Karatas, Arzum, Sahin, Serap
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
application of community detection
Big Data
community detection
Complex networks
Cyber terrorism
Deep learning
Smart cities
Task analysis
Taxonomy
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Summary:In the realm of today's real world, information systems are represented by complex networks. Complex networks contain a community structure inherently. Community is a set of members strongly connected within members and loosely connected with the rest of the network. Community detection is the task of revealing inherent community structure. Since the networks can be either static or dynamic, community detection can be done on both static and dynamic networks as well. In this study, we have talked about taxonomy of community detection methods with their shortages. Then we examine and categorize application areas of community detection in the realm of nature of complex networks (i.e., static or dynamic) by including sub areas of criminology such as fraud detection, criminal identification, criminal activity detection and bot detection. This paper provides a hot review and quick start for researchers and developers in community detection area.
DOI:10.1109/IBIGDELFT.2018.8625349