Higher-Order Functional Structure Exploration in Heterogeneous Combat Network Based on Operational Motif Spectral Clustering

Higher-order organization of complex networks is an effective way to understand and model the interactions among multiple individuals in complex systems. With the development of military intelligence, the scale and complexity of operational interactions among equipment are increasing in combat systems. Compared with pairwise connectivity patterns, the higher-order network structure captured by subgraphs can better describe the interactions among multiple equipment. In this article, we propose the concept of “operational motif,” a subgraph pattern composed of multiple connected equipment nodes in combat networks. On this basis, we propose a higher-order functional structure analysis framework for heterogeneous combat networks based on operational motif spectral clustering. It identifies higher-order functional clusters based on multiple operational motifs. Also, it can subdivide functional clusters into subclusters of specified size based on the different granularity requirements of decision makers. Experiments show that the framework is effective and efficient in finding higher-order functional clusters in large-scale combat networks. It can also serve to identify key equipment interaction patterns from the perspective of higher-order organizations of combat networks.