An intermediary probability model for link prediction

Abstract Among the numerous link prediction algorithms in complex networks, similarity-based algorithms play an important role due to promising accuracy and low computational complexity. Apart from the classical CN-based indexes, several interdisciplinary methods provide new ideas to this problem and achieve improvements in some aspects. In this article, we propose a new model from the perspective of an intermediary process and introduce indexes under the framework, which show better performance for precision. Combined with k-shell decomposition, our deeper analysis gives a reasonable explanation and presents an insight on classical and proposed algorithms, which can further contribute to the understanding of link prediction problem.

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