Abstract
We address the problem of predicting link attachments to complex networks. As one approach to this problem, we focus on combining network growth (or information propagation) models with machine learning techniques. In this paper, we present a method for predicting link conversions based on the estimated probability of information propagation on each link. In our experiments using a real blogroll network, we show that the proposed method substantially improved the predictive performance based on the F-measure, in comparison to other methods using some conventional criteria.
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Saito, K., Nakano, R., Kimura, M. (2007). Prediction of Link Attachments by Estimating Probabilities of Information Propagation. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_29
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DOI: https://doi.org/10.1007/978-3-540-74829-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74828-1
Online ISBN: 978-3-540-74829-8
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