Data-Driven Information Perception for Complex Industrial Networks of Service Based on Hierarchical Interactive Graph Variational Inference

This article proposes a hierarchical interactive graph variational inference (HI-GVI) approach to solve the data-driven information perception problem in complex industrial networks of service. In the HI-GVI approach, a multi-layer latent graph structure is organized to describe various influencing factors within the industrial network, and then a hierarchical interaction attention learning method is proposed to learn the latent variables and model the hierarchical interaction process among numerous industrial entities. Furthermore, the HI-GVI approach employs a generative self-supervised framework to obtain low-dimensional variables for downstream industrial tasks, which overcomes the challenge of limited industrial labels. The advantage and performance of the HI-GVI approach are demonstrated by addressing three different downstream tasks and are assessed in four real-world datasets.