Community Detection on Social Network – A Survey
Greeshma T S1, Subu Surendren2
1Greeshma T S, Department of Computer Science, Sree Chitra Thirunal College of Engineering Trivandrum (Kerala), India.
2Subu Surendren, Department of Computer Science, Sree Chitra Thirunal College of Engineering Trivandrum (Kerala), India.
Manuscript received on 13 August 2016 | Revised Manuscript received on 20 August 2016 | Manuscript Published on 30 August 2016 | PP: 1-3 | Volume-5 Issue-6, August 2016 | Retrieval Number: F4652085616/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social network is an important application in the internet which represent the geographically dispersed users. Social network provides a variety of methods for explaining patterns and entities. Social networks are mostly represented as graphs, which contain nodes and edges. Nodes are used to represent actors such as people and organizations whereas edges show the relationship between these nodes. Several data sources involved in the social network forms communities which work in self-descriptive manner. A collection of nodes which are connected by edges with high similarity is called a community. The community detection in social network, intend to partition the the graph with dense region which correspond to closely related entities. The selection of data sources and determination of community detection approaches can enhance the accuracy, efficiency and scalability of community. In this survey, different community detection approaches are discussed.
Keywords: Social Network, Community Detection, Community Structure
Scope of the Article: Social Network