Understanding Power-Law Degree Distributions in Random Graphs

This section provides a theoretical justification for WormHole’s strong performance by analyzing its behavior on Chung-Lu random graphs—models known for their power-law degree distributions common in real-world social networks. Drawing on existing theorems, the analysis shows that under specific degree and diameter conditions, WormHole maintains sublinear complexity and consistent accuracy, explaining its scalability and efficiency in practice.