Affiliations: LIP6 – CNRS and Université Pierre et Marie Curie, Paris, France | [x] IRPI-CNR, Torino, Italy | [y] University of Torino, Torino, Italy | [z] University of Paris-Nord, Paris, France
Correspondence:
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Corresponding author: Oussama Allali, LIP6 – CNRS and Université Pierre et Marie Curie (UPMC – Paris 6), 4 place Jussieu, 75252 Paris cedex 05, France. E-mail: Oussama.Allali@lip6.fr.
Abstract: Many real-world complex networks, like actor-movie or file-provider relations, have a bipartite nature and evolve over time. Predicting links that will appear in them is one of the main approach to understand their dynamics. Only few works address the bipartite case, though, despite its high practical interest and the specific challenges it raises. We define in this paper the notion of internal links in bipartite graphs and propose a link prediction method based on them. We thoroughly describe the method and its variations, and experimentally compare it to a basic collaborative filtering approach. We present results obtained for a typical practical case. We reach the conclusion that our method performs very well, and we study in details how its parameters may influence obtained results.
Keywords: Complex networks, internal link prediction, bipartite graphs
