Link Prediction for Question-Answering Bulletin Boards
نویسندگان
چکیده
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 the network can be predicted, it is quite useful for encouraging communications among users. This paper describes a method for predicting links based on the structure of social network. The method is based on an assumption that new links can be predicted better using both graph proximity measures and the weights of existing links in a social network. The data of Yahoo! Chiebukuro (Japanese Yahoo! Answers) are used for our experiments. The results show that our method is better than previous approaches, especially when target social network is sufficiently dense.
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