Exploiting user-to-user topic inclusion degree for link prediction in social-information networks

作者：

Highlights：

• Introduce a model to fuse network and content in social-information networks.

• A new topic-oriented measurement is defined to measure the user-user relation.

• Rich content is effectively encoded in a constructed sparse network.

• Link prediction is significantly improved in social-information networks.

摘要

•Introduce a model to fuse network and content in social-information networks.•A new topic-oriented measurement is defined to measure the user-user relation.•Rich content is effectively encoded in a constructed sparse network.•Link prediction is significantly improved in social-information networks.

论文关键词：Link prediction,Fusion model,Topic inclusion degree,Network data analysis

论文评审过程：Received 22 October 2017, Revised 26 April 2018, Accepted 26 April 2018, Available online 28 April 2018, Version of Record 12 May 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.04.034