Science Daily February 10, 2021
Many natural and human-made networks, such as computer, biological or social networks have a connectivity structure that critically shapes their behavior. Existing algorithms that create connected networks with a specific number of connections for each node suffer from uncontrolled bias potentially compromising the conclusions of the study. Researchers in Germany have developed a new method for the random sampling of connected networks with a specified degree sequence considering both the case of simple graphs and that of loopless multigraphs. Their method builds on a recently introduced novel sampling approach that constructs graphs with given degrees independently and extends it to incorporate the constraint of connectedness. They demonstrated their sampling method on a realistic scale-free example, and degree sequences of connected real-world networks to show that enforcing connectedness can significantly alter the properties of sampled networks…read more. Open Access TECHNICAL ARTICLEÂ
A typical connected realization of the degree sequence used in the following panels (500 vertices, 519 edges)…Credit: Journal of Physics: Complexity, Volume 2, Number 1