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The link prediction problem for social networks
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Love all, trust a few: link prediction for trust and psycho-social factors in MMOs
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Fast and accurate link prediction in social networking systems
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
sonLP: social network link prediction by principal component regression
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Proximity measures for link prediction based on temporal events
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Prediction of links - both new as well as recurring - in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time. In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches.