STATISTICAL ANALYSIS AND TOPOLOGICAL PROPERTY OF A CLASS OF FRACTAL NETWORKS

The effective representation of complex systems relies heavily on optimizing network models that capture their intricate and dynamic interdependencies. This study introduces a novel category of polygonal, fractal networks characterized by distinct alteration patterns and structural properties. Through a comprehensive examination of the statistical and topological characteristics of these networks, including clustering coefficient, average path length, degree distribution, and network density, we investigate their properties. A rigorous analysis combining mathematical modeling and empirical evaluation reveals that these fractal networks possess emergent scale-free and small-world properties, which are essential for conferring robustness and efficient information flow. Our findings provide valuable insights into the dynamics of complex systems and lay the groundwork for understanding and designing more efficient network-based systems in socioeconomic and biological contexts. This analysis not only theoretically confirms predictions related to fractal and complex networks but also enhances understanding of the adaptability and scalability of these structures.