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
相关论文
共 12 条
[1]  
Ahajjam S., 2015, 2015 IEEE ACS 12 INT, P1
[2]  
Chitlapudi S Rao, MHM K PRASAD COMMUNI
[3]  
Hutair MB, 2016, 2016 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES (BDCAT), P274, DOI 10.1145/3006299.3006342
[4]  
Keyi Shen, 2010, 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), P276, DOI 10.1109/CyberC.2010.57
[5]  
Kim J, 2015, SIGMOD REC, V44, P37, DOI 10.1145/2854006.2854013
[6]   Community detection in complex networks via adapted Kuramoto dynamics [J].
Maia, Daniel M. N. ;
de Oliveira, Joao E. M. ;
Quiles, Marcos G. ;
Macau, Elbert E. N. .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2017, 53 :130-141
[7]   Community detection in social networks using user frequent pattern mining [J].
Moosavi, Seyed Ahmad ;
Jalali, Mehrdad ;
Misaghian, Negin ;
Shamshirband, Shahaboddin ;
Anisi, Mohammad Hossein .
KNOWLEDGE AND INFORMATION SYSTEMS, 2017, 51 (01) :159-186
[8]  
Moosavi SeyedAhmad., 2014, 2014 Iranian Conference on Intelligent Systems (ICIS), P1, DOI DOI 10.1109/IRANIANCIS.2014.6802552
[9]  
Srinivas RS, 2016, INDIAN J SCI TECHNOL, V9
[10]   Discovering and Profiling Overlapping Communities in Location-Based Social Networks [J].
Wang, Zhu ;
Zhang, Daqing ;
Zhou, Xingshe ;
Yang, Dingqi ;
Yu, Zhiyong ;
Yu, Zhiwen .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2014, 44 (04) :499-509