Alexandre Vidmer

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Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network(More)
The recommender system is one of the most promising ways to address the information overload problem in online systems. Based on the personal historical record, the recom-mender system can find interesting and relevant objects for the user within a huge information space. Many physical processes such as the mass diffusion and heat conduction have been(More)
The spreading of information is of crucial importance for the modern information society. While we still receive information from mass media and other non-personalized sources, online social networks and influence of friends have become important personalized sources of information. This calls for metrics to measure the influence of users on the behavior of(More)
As a fundamental challenge in vast disciplines, link prediction aims to identify potential links in a network based on the incomplete observed information, which has broad applications ranging from uncovering missing protein-protein interaction to predicting the evolution of networks. One of the most influential methods rely on similarity indices(More)
–Random walks on bipartite networks have been used extensively to design personal-ized recommendation methods. While aging has been identified as a key component in the growth of information networks, most research has focused on the networks' structural properties and neglected the often available time information. Time has been largely ignored both by the(More)
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