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A link prediction algorithm based on label propagation
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DOI：10.1016/j.jocs.2016.03.017.png)
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Abstract
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The study of link prediction in graph theory has received more and more attention in recent years. Considering the attributes of nodes in online social networks are generally inaccurate, it is very important and efficient to use the network structure characteristics rather than nodes' information to predict edges in networks. In this paper, we present a simple but effective similarity-based prediction strategy based on label propagation, which mimics the communication between people naturally. We perform an experimental comparison of the proposed method against four classic local similarity-based link prediction algorithms using real-world networks. The experimental results show that our method offers higher precision than these well-known approaches. Hence, we can provide more accurate friend recommendations for online social networks and reduce experimental costs in the fields of biology, and better understand the evolution mechanism of complex networks. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Complex networks
Link prediction
Label propagation
Dynamic process
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