Volume 101, Number 1, January 2013
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||21 January 2013|
Hidden link prediction based on node centrality and weak ties
1 Key Laboratory of Universal Wireless Communications (Beijing University of Posts and Telecommunications), Ministry of Education - Beijing, China
2 School of Electronic Engineering and Computer Science Queen Mary, University of London London, UK, EU
Received: 30 September 2012
Accepted: 20 December 2012
Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. In this context, similarity-based algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link prediction, which is based on the node centrality of common neighbors. Three node centralities are discussed: degree, closeness and betweenness centrality. In our model, each common neighbor plays a different role to the node connection likelihood according to their centralities. Moreover, the weak-tie theory is considered for improving the prediction accuracy. Finally, extensive experiments on five real-world networks show that the proposed model can outperform the Common Neighbor (CN) algorithm and gives competitively good prediction of or even better than Adamic-Adar (AA) index and Resource Allocation (RA) index.
PACS: 89.75.Hc – Networks and genealogical trees / 89.20.Ff – Computer science and technology / 89.65.-s – Social and economic systems
© EPLA, 2013
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.