Self-Configured Framework for scalable link prediction in twitter: Towards autonomous spark framework
作者：
Highlights：
• Present state-of-the-art link prediction applications in large-scale social networks.
• Design a self-configured framework for a scalable link prediction in large-scale social networks.
• Implement the proposed framework with Apache Spark to demonstrate the improvement of scalable link prediction.
• Present the overall experimental results in terms of the performance and efficiency.
• Highlight findings, limitations, and future directions in social networks link prediction.
摘要
•Present state-of-the-art link prediction applications in large-scale social networks.•Design a self-configured framework for a scalable link prediction in large-scale social networks.•Implement the proposed framework with Apache Spark to demonstrate the improvement of scalable link prediction.•Present the overall experimental results in terms of the performance and efficiency.•Highlight findings, limitations, and future directions in social networks link prediction.
论文关键词：Self-Configured Framework,Link prediction,Social network,Large-scale
论文评审过程：Received 5 January 2022, Revised 20 May 2022, Accepted 14 August 2022, Available online 20 August 2022, Version of Record 1 September 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109713
