Abstract
In this paper, we study how the observed quality of a network link prediction method applied to a part of a network can be further used for the analysis of the whole network. Namely, we first show that it can be determined for a part of the network which topological features of node pairs lead to a certain level of link prediction quality. Based on it, we construct a link predictability (prediction quality) classifier for the network links. This is further used in the other part of the network for controlling the link prediction quality typical for the method and the network. The actual link prediction method is not used then already. Experiments with synthetic and real-world networks show a good performance of the proposed pipeline. The source code, the datasets and the results related to our study are publicly available on GitHub.
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Notes
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It is also called link reconstruction sometimes. For this reason, we use these terms interchangeably in what follows.
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This research is financially supported by the Russian Science Foundation, Agreement 17-71-30029, with co-financing of Bank Saint Petersburg, Russia.
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Stavinova, E., Evmenova, E., Antonov, A., Chunaev, P. (2022). Link Predictability Classes in Complex Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_32
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