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