Survey on Efficient Community Detection in Social Networks

被引:0
|
作者
Suryateja, G. [1 ]
Palani, Saravanan [1 ]
机构
[1] SASTRA Univ, Sch Comp, Tanjore, India
来源
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SUSTAINABLE SYSTEMS (ICISS 2017) | 2017年
关键词
community detection; social networks; similar interests; pattern mining Big data analysis;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Social networks like Twitter, Facebook are the most visited sites in the internet. These websites has large amount of data about the people and link between them. Structure of community is the main crucial part of social networks. It has very wide range of applications in computer science, biology and social sciences. Community detection will shows how the structure of the links will have the impact on the people and the relationship among them. For the purpose of community discovery high range of applications is developed for years and years. Social networks plays a major role in dispersal of innovation and information. Social networks became very famous in area of research. In community detection the main work is to divide the network into regions in the graph, in some networks communities can exchange information because the persons in the community have same tastes and desires. These type of communities are used in variety of applications of network analysis like customer segmentation, link reference, recommendations and vertex labelling. This survey will plays an important role in evolution and analysis of community detection in various applications
引用
收藏
页码:93 / 97
页数:5
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