Resolving Power Equipment Data Inconsistency via Heterogeneous Network Alignment

This paper studies the problem of resolving data inconsistency from multiple sources in managing related to power equipment for China’s state grid corporation. proposes automatically align inconsistent devices sources, i.e., same that have entries with different values each source, by HENGE, a HEtetrogeneous Network GEneration model. HENGE builds into heterogeneous graph, and captures complex physical semantic relationships among devices. combines both feature relational information improves alignment accuracy feature-enhanced residual graph encoder disentangled representation learning. is capable learn small amount labeled data, through uniformity autoencoder trained on an unsupervised generation task. Experiments two real-world datasets demonstrate capability device sources.