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KDD
2010
ACM

Cold start link prediction

9 years 2 months ago
Cold start link prediction
In the traditional link prediction problem, a snapshot of a social network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely to appear in the future. In this paper, we introduce cold start link prediction as the problem of predicting the structure of a social network when the network itself is totally missing while some other information regarding the nodes is available. We propose a two-phase method based on the bootstrap probabilistic graph. The first phase generates an implicit social network under the form of a probabilistic graph. The second phase applies probabilistic graph-based measures to produce the final prediction. We assess our method empirically over a large data collection obtained from Flickr, using interest groups as the initial information. The experiments confirm the effectiveness of our approach. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications - Data mining General Terms...
Vincent Leroy, Berkant Barla Cambazoglu, Francesco
Added 15 Aug 2010
Updated 15 Aug 2010
Type Conference
Year 2010
Where KDD
Authors Vincent Leroy, Berkant Barla Cambazoglu, Francesco Bonchi
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