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

Heping Zhang, Sicong Zhang, Xiaoyao Xie, Taihua Zhang, Guojun Yu

Abstract: 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 eight 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.