A Probabilistic Link Prediction Model in Time-Varying Social Networks
Abstract
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%.
Recommended Citation
S. Das and S. K. Das, "A Probabilistic Link Prediction Model in Time-Varying Social Networks," Proceedings of the 2017 IEEE International Conference on Communications (2017, Paris, France), Institute of Electrical and Electronics Engineers (IEEE), May 2017.
The definitive version is available at https://doi.org/10.1109/ICC.2017.7996909
Meeting Name
2017 IEEE International Conference on Communications, ICC 2017 (2017: May 21-25, Paris, France)
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
International Standard Book Number (ISBN)
978-1-4673-8999-0
International Standard Serial Number (ISSN)
1550-3607
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 May 2017
Comments
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.