Weighted Network with Uniform Weight Distribution and Power-Law Node Strength

In weighted networks, we can implement a model where edge weights follow uniform distribution while node strengths exhibit power-law distribution. This framework enables generation of degree distribution plots and strength distribution graphs through computational methods, providing deeper insights into network topology and characteristics. Algorithmically, this can be achieved using graph theory libraries like NetworkX in Python, where weight assignment employs random uniform sampling while node strength follows preferential attachment mechanisms. Such models facilitate the study of nodal connection patterns and interaction dynamics, helping researchers analyze network behaviors across domains - from social network contact patterns to physical network device interconnections. Beyond academic research, this model has practical applications in optimizing and designing diverse network systems through simulation-based approaches that involve parameter tuning and statistical validation of distribution properties.