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Identification of Key Nodes in Equipment System Network based on Function Chain

With the rapid development of modern military technology, the combat mode has been upgraded from traditional platform combat to system-level confrontation. In traditional combat network, node function is single and which is no proper assignment of tasks. The equipment system network studied in this paper contains many different functional nodes, which constitute a huge heterogeneous complex network. Most of the key node identification methods are analyzed from the network topology structure, such as degree, betweenness, K-shell, PageRank, etc. However, with the change of network topology, the identification effect of these methods will be biased. In this paper, we construct a nodal attack sequence, Consider the change of the number of effective OODA chains in the equipment system network after the nodes in the sequence are attacked. And combined with the improved Gray Wolf optimization algorithm, this paper proposes a key node evaluation model of equipment system network based on function chain - IABFI. Experimental results show that the proposed method is more effective, accurate, and applicable to different network topologies than other key node identification methods.