document: 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 .
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