Link Prediction of Social Networks Based on Weighted Proximity Measures

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 85 - 88
• Main Authors: Murata, Tsuyoshi, Moriyasu, Sakiko
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Modeling and simulation > Model development and analysis; Computing methodologies > Modeling and simulation > Model development and analysis > Model verification and validation; Human-centered computing > Collaborative and social computing; Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Web-based interaction; Information systems > Information retrieval; Information systems > Information retrieval > Evaluation of retrieval results
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.71

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.