Identification of Key Nodes in Complex Networks Based on Network Representation Learning

Recently, some research has utilized machine learning methods to identify critical nodes in complex networks. However, existing approaches often lack a comprehensive consideration of network structural features during node feature extraction. Benefiting from the powerful feature extraction capability of network representation learning methods, a simple and effective algorithm for identifying key nodes in complex networks, termed Network Representation Learning and Key Node Identification (NRL_KNI), is proposed. The NRL_KNI algorithm utilizes network embedding techniques for learning node feature representations, followed by clustering and the utilization of quota-based limited sampling to obtain sampled nodes. Subsequently, these sampled nodes are employed to train a regression model for predicting the diffusion capability of unsampled nodes. To rank node influences, a Local Structure Influence Score (LSIS) based on the local structure is introduced to evaluate nodes' final impact. Experimental results on six real-world datasets demonstrate that the NRL_KNI algorithm generally outperforms traditional centrality methods and network representation learning-based methods in terms of the Jaccard similarity coefficient and Kendall's Tau correlation coefficient evaluation metrics.