Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. 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. 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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机译:问题解答公告板(QABB),例如Yahoo!答案和Windows Live QnA最近越来越流行。 QABB上的通信将用户连接起来,整个连接可以视为一个社交网络。如果可以预测社交网络的发展,那么对于鼓励用户之间的交流非常有用。本文介绍了一种基于社交网络的加权接近度度量来预测链接的改进方法。该方法基于这样的假设:可以通过使用图接近度度量和社交网络中现有链接的权重来更好地估计节点之间的邻近度。为了显示我们方法的有效性,Yahoo!的数据Chiebukuro(日语Yahoo!答案)用于我们的实验。结果表明,我们的方法优于以前的方法,尤其是当目标社交网络足够密集时。
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