A weighted network community detection algorithm based on deep learning

作者：

Highlights：

• We propose a community detection algorithm based on a deep sparse autoencoder.

• We combine the path weight matrix with the weighted adjacent paths of the node to obtain the similarity matrix.

• The feature matrix has stronger ability to express the features of the network.

• The proposed algorithm can more accurately identify community structures.

摘要

•We propose a community detection algorithm based on a deep sparse autoencoder.•We combine the path weight matrix with the weighted adjacent paths of the node to obtain the similarity matrix.•The feature matrix has stronger ability to express the features of the network.•The proposed algorithm can more accurately identify community structures.

论文关键词：Community detection,Weighted network,Deep learning,Second-order neighbors

论文评审过程：Received 10 December 2020, Revised 7 January 2021, Accepted 10 January 2021, Available online 20 February 2021, Version of Record 20 February 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126012