Research on Self-Learning Method for Key Nodes Identification in Heterogeneous Networks

Identifying key nodes in heterogeneous networks is both theoretically important and practically valuable. Traditional methods require precise parameters and constraints, limiting adaptability and autonomy. To address this, we propose the deep reinforcement learning–based heterogeneous network key nodes identification (DRLKHN) method, a self-learning method for identifying key nodes. DRLKHN autonomously learns strategies for identifying key nodes, utilizing a graph convolution network (GCN) for feature extraction and designing action and state space vectors. Experimental results show that DRLKHN outperforms traditional methods like high degree adaptive (HDA), high eigenvector adaptive (HEA), high closeness adaptive (HCA) and high PageRank adaptive (HPA) in simulated networks. In the real-world force, intelligence, networking and C2 (FINC) network, DRLKHN improves performance by 28.6%, 32.2%, 12.7% and 36.3% over HDA, HEA, HCA and HPA, respectively. Despite its relatively high time complexity, DRLKHN effectively integrates the GCN and reinforcement learning to manage complex relationships in graph data, providing intelligent decision support for identifying key nodes in real networks.