Link prediction in dynamic social networks by integrating different types of information

Link prediction in social networks has attracted increasing attention in various fields such as sociology, anthropology, information science, and computer science. Most existing methods adopt a static graph representation to predict new links. However, these methods lose some important topological i...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2015-06, Vol.42 (4), p.738-750
Main Authors: Ibrahim, Nahla Mohamed Ahmed, Chen, Ling
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Communities
Computer Science
Dynamics
Eigenvectors
Graph representations
Links
Machine learning
Machines
Manufacturing
Mathematical models
Mechanical Engineering
Methods
Networks
Processes
Social network analysis
Social networks
Sociology
Temporal logic
Time series
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Summary:Link prediction in social networks has attracted increasing attention in various fields such as sociology, anthropology, information science, and computer science. Most existing methods adopt a static graph representation to predict new links. However, these methods lose some important topological information of dynamic networks. In this work, we present a method for link prediction in dynamic networks by integrating temporal information, community structure, and node centrality in the network. Information on all of these aspects is highly beneficial in predicting potential links in social networks. Temporal information offers link occurrence behavior in the dynamic network, while community clustering shows how strong the connection between two individual nodes is, based on whether they share the same community. The centrality of a node, which measures its relative importance within a network, is highly related with future links in social networks. We predict a node’s future importance by eigenvector centrality, and use this for link prediction. Merging the typological information, including community structure and centrality, with temporal information generates a more realistic model for link prediction in dynamic networks. Experimental results on real datasets show that our method based on the integrated time model can predict future links efficiently in temporal social networks, and achieves higher quality results than traditional methods.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-014-0631-0