| Summary: | Knowledge graph, which is a rapidly developing technology, provides strong support in business and engineering. Knowledge graph plays an important role in recommendations and decision-making, while in the electric power industry, there would be more possibilities for knowledge graph to be utilized. However, as a complex cause-and-effect network, the electric power domain knowledge graph has massive nodes, heterogeneous edges, and sparse structures. Thus, it requires human effort to process data, while quality and accuracy cannot be guaranteed. We propose a novel graph computing-based knowledge reasoning method that takes into account the sparsity of the electric power domain knowledge graph to solve the aforementioned problems and achieve improved accuracy of graph classification and knowledge reasoning tasks. The Haar basis is constructed to realize fast calculation, while the multi-scale network structure is introduced to assure classification accuracy and generalization. We evaluate the proposed algorithm on the NCI-1, CEPRI_UHVP, and CEPRI_EQUIP databases. Simulation results demonstrate its superior performance in terms of accuracy and loss.
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