Link prediction in a bipartite network using Wikipedia revision information

Yang Jui Chang, Hung-Yu Kao

研究成果: Conference contribution

17 引文 斯高帕斯(Scopus)

摘要

We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.

原文English
主出版物標題Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
頁面50-55
頁數6
DOIs
出版狀態Published - 2012 十二月 1
事件2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan
持續時間: 2012 十一月 162012 十一月 18

出版系列

名字Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

Other

Other2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
國家/地區Taiwan
城市Tainan
期間12-11-1612-11-18

All Science Journal Classification (ASJC) codes

  • 人工智慧

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