Link Prediction in Social Network Using Co-clustering Based Approach

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...

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Bibliographic Details
Main Authors: Hoseini, E., Hashemi, S., Hamzeh, A.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Bigraph Spectral Co-clustering
Bipartite graph
Clustering algorithms
Equations
Link prediction
Mathematical model
Partitioning algorithms
Prediction algorithms
Spectral Graph clustering
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Summary: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.
DOI:10.1109/WAINA.2012.189