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

ABSTRACT 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.