文章基本信息

标题：Object Ranking in Evolutional Networks via Link Prediction
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作者：Taiki Miyanishi ; Kazuhiro Seki ; Kuniaki Uehara 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2012
卷号：27
期号：3
页码：223-234
DOI：10.1527/tjsai.27.223
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper proposes a framework to predict future significance or importance of nodes of a network through link prediction. The network can be of any kind, such as a co-authorship network where nodes are authors and co-authors are linked by edges. In this example, predicting significant nodes means to discover influential authors in the future. There are existing approaches to predicting such significant nodes in a future network and they typically rely on existing relationships between nodes. However, since such relationships are dynamic and would naturally change over time (e.g., new co-authorship continues to emerge), approaches based only on the current status of the network would have limited potentiality to predict the future. In contrast, our proposed approach first predicts future links between nodes by multiple supervised classifiers and applies the RankBoost algorithm for combining the predictions such that the links would lead to more precise predictions of a centrality (significance) measure of our choice. To demonstrate the effectiveness of our proposed approach, a series of experiments are carried out on the arXiv (HEP-Th) citation data set.
关键词：link analysis ; rankboost ; link prediction ; object ranking