A Probabilistic Link Prediction Model in Time-Varying Social Networks


One of the most intriguing aspects of network analysis is how links or interactions occur over time between a pair of nodes and whether we can have a model to accurately predict the occurrence of links ahead of time, and with what accuracy. In contrast to the existing approaches, this paper proposes a novel Markov prediction model over the time-varying graph of an underlying social network. The model considers the effect of multiple time scales in leveraging temporal analysis for link prediction. The analysis considers fine-grained and coarse-grained time scales, along with associated local (links) and semi-global (clusters) structural evolution, respectively. The model takes into account correlated evolution and rate of evolution in selecting start and end nodes, and the corresponding interaction probability. Finally, we use temporal data of two heavily dynamic real world social networks (e.g., Twitter and Facebook), and a relatively lesser dynamic network (e.g., DBLP) to demonstrate the prediction accuracy that our Markov model outperforms two recent dynamic approaches in the range of 7.5% to 19.81%.

Meeting Name

2017 IEEE International Conference on Communications, ICC 2017 (2017: May 21-25, Paris, France)


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research


This work is partially supported by NSF grants under award numbers CCF-1533918 and and CBET-1609642, and also by a grant from the Intelligent Systems Center at Missouri S&T.

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International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


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© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2017