The field of complex network analysis is moving towards the development of more sophisticated methods for identifying influential nodes and understanding the structural properties of networks. Researchers are exploring new approaches that combine machine learning techniques with traditional network analysis methods to improve the accuracy and efficiency of node ranking and influence maximization. Notably, the use of deep learning models and hypergraph-based methods is becoming increasingly popular. These advances have the potential to improve our understanding of complex systems and enable more effective interventions in a wide range of domains. Some noteworthy papers in this area include: A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks, which proposes a novel hybrid model that achieves state-of-the-art performance in node ranking accuracy while operating at a highly reasonable time. HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximization, which introduces a native active learning framework designed specifically for hypergraphs and achieves significant improvements in performance and efficiency over existing methods.