Fast approach for link prediction in complex networks based on graph decomposition

Abstract

Social networks such as Facebook, Twitter, etc. have dramatically increased in recent years. These databases are huge and their use is time consuming. In this work, we present an optimal calculation in graph mining for link prediction to reduce the runtime. For that purpose, we propose a novel approach that operates on the connected components of a network instead of the whole network. We show that thanks to this decomposition, the results of all link prediction algorithms using local and path-based similarity measure scan be achieved with much less amount of computations and hence within much shorter runtime. We show that this gain depends on the distribution of nodes in components and may be captured by the Gini and the variance measures. We propose a parallel architecture of the link prediction process based on the connected components decomposition. To validate this architecture, we have carried out an experimental study on a wide range of well-known datasets. The obtained results clearly confirm the efficiency of exploiting the decomposition of the network into connected components in link prediction.