Prediction of link attachments by estimating probabilities of information propagation

Authors:
Kazumi Saito;Ryohei Nakano;Masahiro Kimura
Affiliations:
NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan;Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya, Japan;Department of Electronics and Informatics, Ryukoku University, Otsu, Shiga, Japan
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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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.