Kevin E. Bassler

TL;DR: In this article, the authors propose that the emergence of many scale-free networks is tied to the efficiency of transport and flow processing across these structures, and show that for large networks on which flows are influenced or generated by gradients of a scalar distributed on the nodes, scale free structures will ensure efficient processing, whereas structures that are not scale free, such as random graphs, will become congested.

TL;DR: This work presents a heuristic algorithm that balances traffic on a network by minimizing the maximum node betweenness with as little path lengthening as possible, thus being useful in cases when networks are jamming due to node congestion.

TL;DR: This work proposes an efficient, polynomial time algorithm that generates statistically independent graph samples with a given, arbitrary, degree sequence, and argues that for large, and for degree sequences admitting many realizations, the sample weights are expected to have a lognormal distribution.

TL;DR: This work considers six real networks and finds that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network.

TL;DR: The realizability of scale-free networks with a given degree sequence is studied, showing that the fraction of realizable sequences undergoes two first-order transitions at the values 0 and 2 of the power-law exponent.