Key Node Recognition Method Based on Layer Partitioning and Node Features
Critical node detection has become an important research domain in complex networks;however,current critical node detection methods suffer from high algorithm time complexity,inaccurate critical node sets obtained,and insufficient consideration of node centrality indicators.Based on this,this study first presents a critical node-detection framework based on layer and node features.This framework introduces a layer-partitioning-based method to enhance the efficiency of selecting the initial coverage set of critical nodes,allowing for the calculation of the initial node set in linear time.Subsequently,the nodes were added back to the original network through node centrality feature indicators until the number of nodes in the solution set met a predefined threshold.To overcome the challenge of local optima during node re-addition,a node centrality index was developed,taking into account the network topology and various node attributes.Experimental results from real networks,after comparing five initial coverage set selection algorithms and five centrality indices,indicate that the proposed method,utilizing layer partitioning and node features,offers more accurate and efficient detection of critical nodes in different network types with enhanced robustness and performance over existing methods.
万方数据
维普
