Cold Start Link Prediction

Abstract : In the traditional link prediction problem, a snapshot of a so- cial network is used as a starting point to predict, by means of graph-theoretic measures, the links that are likely to ap- pear 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 avail- able. We propose a two-phase method based on the bootstrap probabilistic graph. The first phase generates an implicit so- cial 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 empiri- cally over a large data collection obtained from Flickr, using interest groups as the initial information. The experiments confirm the effectiveness of our approach.
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Communication dans un congrès
The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Jul 2010, Washington DC, United States. 12 p, 2010
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Vincent Leroy, B. Barla Cambazoglu, Francesco Bonchi. Cold Start Link Prediction. The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Jul 2010, Washington DC, United States. 12 p, 2010. 〈inria-00485619〉

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