Scale-Free Network Implementation Using Roulette Wheel Selection Algorithm

This article provides an in-depth exploration of complex network fundamentals and the implementation of scale-free networks using the roulette wheel selection algorithm. Complex networks consist of numerous interconnected nodes and edges with intricate relationship patterns. Specifically, scale-free networks exhibit high connectivity and scale-free properties, where node degree distributions follow a power-law distribution. The roulette wheel selection algorithm determines connection probabilities based on node degrees, effectively facilitating the construction of scale-free networks. From a programming perspective, the algorithm typically involves: calculating cumulative probability distributions from node degrees, generating random numbers to select connection targets, and implementing preferential attachment mechanisms where higher-degree nodes have greater connection probabilities. Key implementation functions may include degree distribution calculations, probability normalization, and iterative edge formation loops. Through detailed explanations in this article, readers will gain deeper insights into complex networks and the practical implementation of scale-free networks using the roulette wheel algorithm, thereby enhancing their understanding and mastery of these concepts.