%0 Conference Paper %1 Murata:2007:LPS:1331740.1331799 %A Murata, Tsuyoshi %A Moriyasu, Sakiko %B Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence %C Washington, DC, USA %D 2007 %I IEEE Computer Society %K networks prediction social %P 85--88 %R 10.1109/WI.2007.71 %T Link Prediction of Social Networks Based on Weighted Proximity Measures %U http://dx.doi.org/10.1109/WI.2007.71 %X 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. %@ 0-7695-3026-5

@inproceedings{Murata:2007:LPS:1331740.1331799, abstract = {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.}, acmid = {1331799}, added-at = {2012-09-21T19:02:12.000+0200}, address = {Washington, DC, USA}, author = {Murata, Tsuyoshi and Moriyasu, Sakiko}, biburl = {https://www.bibsonomy.org/bibtex/2eb046966919c7559c23ff86f8a404e2e/kibanov}, booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence}, description = {Link Prediction of Social Networks Based on Weighted Proximity Measures}, doi = {10.1109/WI.2007.71}, interhash = {1345bdebd46dbf8eff48d8d48d7ebfde}, intrahash = {eb046966919c7559c23ff86f8a404e2e}, isbn = {0-7695-3026-5}, keywords = {networks prediction social}, numpages = {4}, pages = {85--88}, publisher = {IEEE Computer Society}, series = {WI '07}, timestamp = {2012-09-21T19:02:12.000+0200}, title = {Link Prediction of Social Networks Based on Weighted Proximity Measures}, url = {http://dx.doi.org/10.1109/WI.2007.71}, year = 2007 }