Influence maximization in social networks using transfer learning via graph-based LSTM

作者：

Highlights：

• A novel method for IM is proposed based on transfer learning via a graph-based LSTM.

• Obtained features on the training network fed to GLSTM to learn the model parameters.

• The trained GLSTM model predicts the spreading influence of nodes of target network.

• Simulations on various datasets reveal the improved performance of the proposed work.

摘要

•A novel method for IM is proposed based on transfer learning via a graph-based LSTM.•Obtained features on the training network fed to GLSTM to learn the model parameters.•The trained GLSTM model predicts the spreading influence of nodes of target network.•Simulations on various datasets reveal the improved performance of the proposed work.

论文关键词：Graph-based LSTM,Influence maximization (IM),Node centrality,Information diffusion,Social networks,Transfer learning

论文评审过程：Received 24 October 2021, Revised 1 September 2022, Accepted 2 September 2022, Available online 5 September 2022, Version of Record 15 September 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118770