Social networks have grown exponentially in recent times. Hence, the analysis of social networks has proven to be very useful and has drawn many researchers into this research domain. In particular, link prediction remains as an important problem of social networks. Link Prediction has wide range of applications, where it is used for recommending friends on social networks such as Facebook and also in recommending products to customers in E commerce platforms like Amazon. This paper has proposed a new algorithm for link prediction by incorporating a centrality measure. This algorithm has been applied to real time social networks and it has performed better than the existing baseline methods.
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Link Prediction using Influencer nodes of a Social Network
Semantic Scholar · Computer Science · 2020
Abstract
Social networks have grown exponentially in recent times. Hence, the analysis of social networks has proven to be very useful and has drawn many researchers into this research domain. In particular, link prediction remains as an important problem of social networks. Link Prediction has wide range of applications, where it is used for recommending friends on social networks such as Facebook and also in recommending products to customers in E commerce platforms like Amazon. This paper has proposed a new algorithm for link prediction by incorporating a centrality measure. This algorithm has been applied to real time social networks and it has performed better than the existing baseline methods.