Fitness Model in Complex Networks

The fitness model in complex networks serves as a crucial tool for investigating interactions between network nodes, with the Barabási-Albert (BA) model being one of the most widely applied frameworks. Recent scholarly improvements to the BA model incorporate exponential distribution to characterize node degree distributions. This enhancement enables more accurate simulations of real-world networks since many practical scenarios exhibit node degree distributions that deviate from power-law patterns. The implementation typically involves modifying the preferential attachment mechanism by assigning fitness values to nodes sampled from an exponential distribution, then using these values to determine connection probabilities. Key algorithmic components include fitness-based attachment functions and degree distribution validation modules. By employing exponential distributions, researchers can better model network structures in diverse scenarios, consequently improving the understanding of nodal behavior and evolutionary patterns in complex systems.