Prediction of link attachments by estimating probabilities of information propagation

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. © Springer-Verlag Berlin Heidelberg 2007.