Identification of spreading influence nodes via multi-level structural attributes based on the graph convolutional network

作者：

Highlights：

• The graph convolutional network is introduced to identify spreading influence nodes.

• The structure properties of networks at multiple levels are taken into account.

• The proposed model trained by small networks can make predictions in large networks.

• Three-channel inputs are constructed to preserve different structural information.

摘要

•The graph convolutional network is introduced to identify spreading influence nodes.•The structure properties of networks at multiple levels are taken into account.•The proposed model trained by small networks can make predictions in large networks.•Three-channel inputs are constructed to preserve different structural information.

论文关键词：Complex networks,Spreading influence,Graph convolutional network,Community structure,k-core value

论文评审过程：Received 27 November 2021, Revised 31 March 2022, Accepted 3 May 2022, Available online 13 May 2022, Version of Record 18 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117515