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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Allali, O. Magnien, C. Latapy, M. |
| Copyright Year | 2011 |
| Description | Author affiliation: LIP6 - CNRS and Université Pierre et Marie Curie (UPMC - Paris 6), 4 place Jussieu, 75252 cedex 05- France (Allali, O.; Magnien, C.; Latapy, M.) |
| Abstract | Many real-world complex networks, like client-product or file-provider relations, have a bipartite nature and evolve during time. Predicting links that will appear in them is one of the main approach to understand their dynamics. Only few works address the bipartite case, though, despite its high practical interest and the specific challenges it raises. We define in this paper the notion of internal links in bipartite graphs and propose a link prediction method based on them. We describe the method and experimentally compare it to a basic collaborative filtering approach. We present results obtained for two typical practical cases. We reach the conclusion that our method performs very well, and that internal links play an important role in bipartite graphs and their dynamics. |
| Starting Page | 936 |
| Ending Page | 941 |
| File Size | 1360264 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457702495 |
| e-ISBN | 9781457702488 |
| DOI | 10.1109/INFCOMW.2011.5928947 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-04-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Context Peer to peer computing Collaboration Focusing Prediction methods Complex networks Bipartite graph |
| Content Type | Text |
| Resource Type | Article |
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