An overlapping network community partition algorithm based on semi-supervised matrix factorization and random walk

作者：

Highlights：

• We combine the prior content information in the network to construct a must-link matrix and a cannot-link matrix.

• We propose an algorithm based on semi-supervised matrix factorization and random walk.

• The random walk model based on node convergence degree is combined with nonnegative matrix factorization.

摘要

•We combine the prior content information in the network to construct a must-link matrix and a cannot-link matrix.•We propose an algorithm based on semi-supervised matrix factorization and random walk.•The random walk model based on node convergence degree is combined with nonnegative matrix factorization.

论文关键词：Matrix factorization,Random walk,Node convergence degree,Node influence

论文评审过程：Received 16 June 2017, Revised 7 September 2017, Accepted 8 September 2017, Available online 9 September 2017, Version of Record 15 September 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.007