Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 |
| Pages | 50-55 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
| Event | 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan Duration: 2012 Nov 16 → 2012 Nov 18 |
Publication series
| Name | Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 |
|---|
Other
| Other | 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 |
|---|---|
| Country | Taiwan |
| City | Tainan |
| Period | 12-11-16 → 12-11-18 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
Cite this
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Link prediction in a bipartite network using Wikipedia revision information. / Chang, Yang Jui; Kao, Hung-Yu.
Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012. 2012. p. 50-55 6395005 (Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Link prediction in a bipartite network using Wikipedia revision information
AU - Chang, Yang Jui
AU - Kao, Hung-Yu
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84873349121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873349121&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2012.49
DO - 10.1109/TAAI.2012.49
M3 - Conference contribution
AN - SCOPUS:84873349121
SN - 9780769549194
T3 - Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
SP - 50
EP - 55
BT - Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
ER -