DocumentCode :
3699301
Title :
A survey of user classification in social networks
Author :
Gang Bai;Lianzhong Liu;Bo Sun;Jing Fang
Author_Institution :
School of Computer Science and Technology, Beihang University, Beijing, China
fYear :
2015
Firstpage :
1038
Lastpage :
1041
Abstract :
Social networks is a virtual communities and networking platform, where people can create and share views, ideas and experiences freely. Social networks like the real world, filled with colorful crowd. How to use user behavior attributes, basic information, and interactive content in social media to classify users has become a hot topic in the field of social network analysis. In this paper, we present a detailed review in various types of user classification in social networks. We present most common methods based on machine learning and non-machine learning in user classification. The algorithms based on machine learning includes: Bayesian, Decision Tree, Logistics, SVM and KNN. The method of non-machine learning includes: concept of entropy and based on user similarity.
Keywords :
"Entropy","Bayes methods","Decision trees","Support vector machines","Classification algorithms","Social network services","Prediction algorithms"
Publisher :
ieee
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference on
ISSN :
2327-0586
Print_ISBN :
978-1-4799-8352-0
Electronic_ISBN :
2327-0594
Type :
conf
DOI :
10.1109/ICSESS.2015.7339230
Filename :
7339230
Link To Document :
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