Social circle detection based on micro-blogging topic and user follow relationship

As the detection of social circles can help the users find the other users with similar interests in a big data environment to expand their friend circles, our algorithm takes the (implicit) user topic in micro-blog and the (explicit) follow relationship between the users into comprehensive account....

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Bibliographic Details
Published in2016 International Conference on Asian Language Processing (IALP) pp. 222 - 227
Main Authors Jiaying Hou, Zhengtao Yu, Xudong Hong, Liren Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Clustering algorithms
Data mining
Detection algorithms
Micro-blogging Topic
Network topology
Prediction algorithms
Probability distribution
Social Circle
Social network services
User Follow Relationship
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Summary:As the detection of social circles can help the users find the other users with similar interests in a big data environment to expand their friend circles, our algorithm takes the (implicit) user topic in micro-blog and the (explicit) follow relationship between the users into comprehensive account. Firstly, use the supervised-LDA model to extract user topics from micro-blogging data and calculate the similarity between users integrating the follow relationship between them. Then choose the initial clustering center of social circle to cluster user nodes and merge those social circles with a high overlapping degree to detect a social circle finally. The experiment proves that our social circle detection algorithm can not only be used to detect the unknown social circles, but also mine the topics of social circles. Also the comparison with the traditional social circle detection algorithms shows that our algorithm is more effective.
DOI:10.1109/IALP.2016.7875973