Generate Complex Networks with Implementation Code

Complex networks represent a significant research focus in contemporary science. A complex network is a system composed of multiple interconnected nodes, where these nodes can represent diverse entities such as individuals, objects, or concepts. Complex networks find applications across various domains including social networks, transportation systems, and logistics networks. The study of complex networks intersects multiple disciplines including mathematics, physics, and computer science. Implementation typically involves graph theory algorithms where nodes are represented as objects with connectivity properties, and edges are established using probability distributions or predefined rules. Key functions often include network initialization, node connection algorithms (like preferential attachment in scale-free networks), and network property calculations. This research provides deeper insights and practical applications, with code implementations frequently utilizing adjacency matrices or edge lists for network representation, and incorporating algorithms for network generation, analysis, and visualization.