DocumentCode :
525209
Title :
Multi-features link prediction based on matrix
Author :
Guo, Jingfeng ; Guo, Hongwei
Author_Institution :
Coll. of Inf. Sci. & Eng., Yanshan Univ., Qinhuangdao, China
Volume :
1
fYear :
2010
fDate :
25-27 June 2010
Abstract :
Existing link prediction methods have mostly adopted overlay network to represent social network and used topological features or attributive features between two nodes to predict the formation of links. However, the limitation of these methods is that they not only use a single feature for link prediction, but also not take into account the time factor and the importance of features. This paper considered the problem of temporal link prediction on the basis of using various features at the same time. Specifically, it presented a matrix-based method for combining temporal features, weighted attributive features and weighted topological features. Using two different datasets our experiments have confirmed that our approach achieved better performance.
Keywords :
matrix algebra; social networking (online); matrix-based method; multifeatures link prediction; temporal features; temporal link prediction; weighted attributive feature; weighted topological feature; Data mining; Design engineering; Educational institutions; Information science; Matrix decomposition; Prediction algorithms; Prediction methods; Singular value decomposition; Social network services; Time factors; Link prediction; Matrix; Weight features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Design and Applications (ICCDA), 2010 International Conference on
Conference_Location :
Qinhuangdao
Print_ISBN :
978-1-4244-7164-5
Electronic_ISBN :
978-1-4244-7164-5
Type :
conf
DOI :
10.1109/ICCDA.2010.5540852
Filename :
5540852
Link To Document :
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