Influence maximization across heterogeneous interconnected networks based on deep learning
作者：
Highlights：
• DeepIM is the first algorithm which employs deep learning techniques for IM problem.
• Both local and global structure are considered during network feature learning.
• DeepIM is scalable for large scale networks because of parallel network exploration.
• Bridge users have an important role to transfer information across networks.
• Node influence is computed in dynamic networks without recalculation of all nodes.
摘要
•DeepIM is the first algorithm which employs deep learning techniques for IM problem.•Both local and global structure are considered during network feature learning.•DeepIM is scalable for large scale networks because of parallel network exploration.•Bridge users have an important role to transfer information across networks.•Node influence is computed in dynamic networks without recalculation of all nodes.
论文关键词：Influence maximization,Interconnected networks,Network embedding,Deep learning,Relevant users
论文评审过程：Received 17 September 2018, Revised 19 July 2019, Accepted 29 August 2019, Available online 31 August 2019, Version of Record 8 September 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112905
