Second-Order Random Walk in Complex Networks

Abstract

In the era of complex networks, it is important to understand the network structure and user behavior. Random walks, especially second-order random walks, provide important tools for analyzing and optimizing networks. This paper combines second-order random walks with graph theory to address the challenges in various networks. The principle, method, and application are expounded in detail through theoretical analysis and practical cases. The research identifies key nodes and discovers communities in social and academic networks to provide a scientific basis for information management and precision marketing. However, challenges such as computational pressure and parameter selection remain. Future solutions include leveraging distributed computing, building theoretical frameworks, and using machine learning to optimize parameters and improve data quality. Overall, this study provides a new perspective for complex network analysis and lays a foundation for further development. Second-order random walk has been effective in many fields, and it is expected to promote intelligent development in more complex network scenarios in the future.