  Sampling a network with a given probability distribution has been identified
as a useful operation. In this paper we propose distributed algorithms for
sampling networks, so that nodes are selected by a special node, called the
\emph{source}, with a given probability distribution. All these algorithms are
based on a new class of random walks, that we call Random Centrifugal Walks
(RCW). A RCW is a random walk that starts at the source and always moves away
from it.
  Firstly, an algorithm to sample any connected network using RCW is proposed.
The algorithm assumes that each node has a weight, so that the sampling process
must select a node with a probability proportional to its weight. This
algorithm requires a preprocessing phase before the sampling of nodes. In
particular, a minimum diameter spanning tree (MDST) is created in the network,
and then nodes' weights are efficiently aggregated using the tree. The good
news are that the preprocessing is done only once, regardless of the number of
sources and the number of samples taken from the network. After that, every
sample is done with a RCW whose length is bounded by the network diameter.
  Secondly, RCW algorithms that do not require preprocessing are proposed for
grids and networks with regular concentric connectivity, for the case when the
probability of selecting a node is a function of its distance to the source.
  The key features of the RCW algorithms (unlike previous Markovian approaches)
are that (1) they do not need to warm-up (stabilize), (2) the sampling always
finishes in a number of hops bounded by the network diameter, and (3) it
selects a node with the exact probability distribution.
