Abstract
Social networks have a dynamic nature so their structures change over time. In this paper, we propose a new evolutionary method to predict the state of a network in the near future by extracting knowledge from its current structure. This method is based on the fact that social networks consist of communities. Observing current state of a given network, the method calculates the probability of a relationship between each pair of individuals who are not directly connected to each other and estimate the chance of being connected in the next time slot. We have tested and compared the method on one synthetic and one large real dataset with 117 185 083 edges. Results show that our method can predict the next state of a network with a high rate of accuracy.
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This work is partially supported by a Cross-Border Institute (CBI) Research Grant.
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Zadeh, P.M., Kobti, Z. (2016). A Knowledge Based Framework for Link Prediction in Social Networks. In: Gyssens, M., Simari, G. (eds) Foundations of Information and Knowledge Systems. FoIKS 2016. Lecture Notes in Computer Science(), vol 9616. Springer, Cham. https://doi.org/10.1007/978-3-319-30024-5_14
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DOI: https://doi.org/10.1007/978-3-319-30024-5_14
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