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...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Vincent Leroy, Berkant Barla Cambazoglu, Francesco Bonchi
• Keywords: Data Mining, KDD 2010, Link Prediction, Probabilistic Graph, Social Networks