Complex Network Analysis: Problems, Applications and Techniques

Complex networks, represented as graphs, serve as powerful models for understanding real-world systems composed of interacting entities. These networks offer valuable insights into both their structural and dynamic properties. This study concentrates on three fundamental aspects of complex network analysis: centrality, link prediction, and community detection. Centrality focuses on identifying influential nodes within the network, link prediction aims to forecast potential future connections, and community detection uncovers cohesive substructures. Through a thorough review of relevant literature, an exploration of practical applications, and an evaluation of benchmark datasets, this work presents a comprehensive analysis of these critical challenges and assesses the performance of widely utilized algorithms.