  In this article, we explicitly derive the limiting degree distribution of the
shortest path tree from a single source on various random network models with
edge weights. We determine the asymptotics of the degree distribution for large
degrees of this tree and compare it to the degree distribution of the original
graph. We perform this analysis for the complete graph with edge weights that
are powers of exponential random variables (weak disorder in the stochastic
mean-field model of distance), as well as on the configuration model with
edge-weights drawn according to any continuous distribution. In the latter, the
focus is on settings where the degrees obey a power law, and we show that the
shortest path tree again obeys a power law with the same degree power-law
exponent. We also consider random $r$-regular graphs for large $r$, and show
that the degree distribution of the shortest path tree is closely related to
the shortest path tree for the stochastic mean-field model of distance. We use
our results to shed light on an empirically observed bias in network sampling
methods. This is part of a general program initiated in previous works by
Bhamidi, van der Hofstad and Hooghiemstra [Ann. Appl. Probab. 20 (2010)
1907-1965], [Combin. Probab. Comput. 20 (2011) 683-707], [Adv. in Appl. Probab.
42 (2010) 706-738] of analyzing the effect of attaching random edge lengths on
the geometry of random network models.
