A Survey on Community Detection Algorithm and Its Applications
Main Article Content
Abstract
Modern network science has made great improvements in the analysis of a large dynamic world. The existence of a community structure is one of the most prominent factors in these networks. Many algorithms have been proposed to detect structural characteristics and dynamic behaviour of networks over recent years. In this paper, present such a detailed study of recent community detection algorithm techniques such as clustering, modularity, dynamic, overlapped, etc based on various factors and their task in the analysis of the social network. Community detection enables us to evaluate participants with mutual interests or to discover a set of similar people on the basis of an area of interest, proposed a node influence k-nearest neighbours (NI-KNN) algorithm for detecting the community. Community detection is useful in many applications such as Recommendation Systems, Health care, politics, economics, e-commerce, social media, communication network, etc. A comparative analysis of different methods of community detection is also reported.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.