Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and
Windows Live QnA, are gaining popularity recently. Questions are
submitted on QABB and let somebody in the internet answer
them. Communications on QABB connect users, and the overall
connections can be regarded as a social network. If the evolution of
social networks can be predicted, it is quite useful for encouraging
communications among users. Link prediction on QABB can be used for
recommendation to potential answerers.
Previous approaches for link prediction based on structural properties
do not take weights of links into account. This paper describes an
improved method for predicting links based on weighted proximity
measures of social networks. The method is based on an assumption that
proximities between nodes can be estimated better by using both graph
proximity measures and the weights of existing links in a social
network. In order to show the effectiveness of our method, the data
of Yahoo! Chiebukuro (Japanese Yahoo! Answers) are used for our
experiments. The results show that our method outperforms previous
approaches, especially when target social networks are sufficiently
dense.
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