Fast approach for link prediction in complex networks based on graph decomposition
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Date
2023
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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 confrm the efciency of exploiting the decomposition of the network into connected
components in link prediction
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Keywords
Link prediction · Social network · Parallel computing · Graph mining · Local information · Interaction mining · Complex networks