Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability

作者：

Highlights：

• The equivalence between the eigene-decomposition and nonnegative matrix factorization is proven.

• This paper proposes two matrix decomposition factorization algorithms for temporal link prediction.

• The algorithm outperforms the state-of-the-art approaches on various temporal networks.

摘要

•The equivalence between the eigene-decomposition and nonnegative matrix factorization is proven.•This paper proposes two matrix decomposition factorization algorithms for temporal link prediction.•The algorithm outperforms the state-of-the-art approaches on various temporal networks.

论文关键词：Dynamic networks,Nonnegative matrix factorization,Eigenvalues and eigenvector,Temporal link prediction

论文评审过程：Received 27 September 2016, Revised 26 May 2017, Accepted 16 June 2017, Available online 17 June 2017, Version of Record 24 June 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.06.025