Graph Representation Learning in Wireless Communication and Tourist Movement Analysis

Modern wireless systems and urban tourism generate massive streams of interconnected data, making graph-based modeling a natural choice for capturing their underlying structure. This chapter presents how Graph Representation Learning (GRL) can be used to analyze and predict complex interactions in wireless communication networks and tourist movement patterns. It introduces a graph-theoretic framework for extracting and modeling tourist flows from social media data, enabling insights into mobility, attraction popularity, and urban dynamics. The chapter also explores knowledge-driven graph learning approaches for next-generation wireless networks, where GRL supports resource allocation, network optimization, and intelligent communication. Through applications in both wireless systems and smart tourism, the chapter demonstrates how graph embeddings and learning techniques turn large, heterogeneous datasets into actionable insights. Additional application areas highlights the broad impact of GRL across data-driven industries and smart city environments.