Abstract
In this paper, we study how the observed quality of a chosen feature-based link prediction model applied to a part of a large node-attributed network can be further used for the analysis of another part of the network. Namely, we first show that it can be determined on the former part the usage of which features (topological and attributive) of node pairs lead to a certain level of link prediction quality. Based on it, we then construct a link predictability (prediction quality) classifier for the network node pairs that is able to distinguish poorly and highly predictable links by a few selected features of the corresponding nodes. The features are selected to provide a reasonable trade-off between the classifier’s time consumption and quality performance. The classifier is further used in the other part of the network for controlling the link prediction quality typical for the model and the network, without performing the actual link prediction. Our experiments show the good performance of the classifier over all tested real-world networks of various types (at least 0.9208 in terms of ROC-AUC and 0.9224 in terms of Average Precision).
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Notes
Roughly speaking, they indicate the relative importance of predictor variables (features) in linear regression.
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Acknowledgements
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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Antonov, A., Stavinova, E., Evmenova, E. et al. Link predictability classes in large node-attributed networks. Soc. Netw. Anal. Min. 12, 81 (2022). https://doi.org/10.1007/s13278-022-00912-w
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DOI: https://doi.org/10.1007/s13278-022-00912-w