IDS: An Intelligent Data Semantics System for Communication Link Prediction in SIoT

Bo Wang, Zunfu Huang, Dongming Zhao, Kun Huang, Meixian Song, Qinxue Jiang, Naixue Xiong

Abstract: The establishment of communication link in Social Internet of Things is not only determined by personal interests, but also by neighbors’ influences. The influence of each neighbor is often independent and different. However, existing communication link prediction methods have not considered the semantic influence of each neighbor separately. Furthermore, the influence of each neighbor may work on multiple semantic levels, which is also not well considered. We propose a deep learning model to embed the multi-level semantic influence of each neighbor separately to predict communication links in SIoT, named as Intelligent Data Semantics (IDS) system. The IDS system captures the semantics of each neighbor’s influence on both local-level and global-level. The multi-level semantic influence integrated with topology information is then used to predict communication links in SIoT. Extensive experiments on four real social networks demonstrate that IDS outperforms the state-of-the-art models.