AECasN: An information cascade predictor by learning the structural representation of the whole cascade network with autoencoder

作者：

Highlights：

• A deep learning-based model AECasN is proposed for information cascade prediction.

• AECasN learns the representation of the network structure rather than the identity nodes.

• AECasN is applicable to information cascade networks of various sizes.

• Two real-world cascade networks are collected to evaluate the effectiveness of AECasN.

摘要

•A deep learning-based model AECasN is proposed for information cascade prediction.•AECasN learns the representation of the network structure rather than the identity nodes.•AECasN is applicable to information cascade networks of various sizes.•Two real-world cascade networks are collected to evaluate the effectiveness of AECasN.

论文关键词：Information cascade,Deep learning,Network structure,Network representation,Autoencoder

论文评审过程：Received 12 August 2020, Revised 9 November 2021, Accepted 18 November 2021, Available online 4 December 2021, Version of Record 10 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116260