DocumentCode :
1850284
Title :
Link Prediction in Social Network Using Co-clustering Based Approach
Author :
Hoseini, Elham ; Hashemi, Sattar ; Hamzeh, Ali
Author_Institution :
Dept. of Comput. Sci. & Eng., Shiraz Univ., Shiraz, Iran
fYear :
2012
fDate :
26-29 March 2012
Firstpage :
795
Lastpage :
800
Abstract :
This paper introduces an approach to derive whether an individual is related to an item or not. In our approach, the well-known DBLP dataset is used and we try to find some skills that are related to an author that we were not aware of before. To realize our objective, we cluster authors and skills using Spectral Graph Clustering algorithm, then simultaneously obtain user and movie clusters via Bipartite Graph (Bigraph) Spectral Co-clustering approach, and then generate predictions based on the outputs of clustering and co-clustering steps. Accordingly, we utilize clustering and co-clustering advantages to predict the probability of link existing between an author and a skill. Experimental results on DBLP dataset show that our approach works well in the specified task.
Keywords :
graph theory; pattern clustering; probability; spectral analysis; DBLP dataset; bipartite graph; link prediction probability; social network; spectral co-clustering approach; spectral graph clustering algorithm; Algorithm design and analysis; Bipartite graph; Clustering algorithms; Equations; Mathematical model; Partitioning algorithms; Prediction algorithms; Bigraph Spectral Co-clustering; Link prediction; Spectral Graph clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on
Conference_Location :
Fukuoka
Print_ISBN :
978-1-4673-0867-0
Type :
conf
DOI :
10.1109/WAINA.2012.189
Filename :
6185492
Link To Document :
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