Predicting information diffusion via deep temporal convolutional networks

作者：

Highlights：

• It proposes a novel deep learning framework for popularity prediction: CasTCN.

• We design a novel mechanism to capture the dynamic structure information of network.

• CasTCN only needs structure and temporal information of networks.

• CasTCN can achieve state-of-the-art in two real-world datasets.

摘要

•It proposes a novel deep learning framework for popularity prediction: CasTCN.•We design a novel mechanism to capture the dynamic structure information of network.•CasTCN only needs structure and temporal information of networks.•CasTCN can achieve state-of-the-art in two real-world datasets.

论文关键词：Social networks,Information diffusion,Dynamic mapping,Temporal convolutional networks

论文评审过程：Received 26 October 2021, Revised 17 February 2022, Accepted 29 March 2022, Available online 31 March 2022, Version of Record 9 April 2022.

论文官网地址：https://doi.org/10.1016/j.is.2022.102045