  We present an implementation and an experimental evaluation of an algorithm
that, given a connected graph G (represented by adjacency lists), estimates in
sublinear time, with a relative error, the Minimum Spanning Tree Weight of G;
the original algorithm has been presented in "Approximating the minimum
spanning tree weight in sublinear time", by Bernard Chazelle, Ronitt Rubinfeld,
and Luca Trevisan (published with SIAM, DOI 10.1137/S0097539702403244). Since
the theoretical performances have already been shown and demonstrated in the
above-mentioned paper, our goal is, exclusively, to experimental evaluate the
algorithm and at last to present the results. Initially we discuss about some
theoretical aspects that arose while we were valuating the asymptotic
complexity of our specific implementation. Some technical insights are then
given on the implementation of the algorithm and on the dataset used in the
test phase, hence to show how the experiment has been carried out even for
reproducibility purposes; the results are then evaluated empirically and widely
discussed, comparing these with the performances of the Prim algorithm and the
Kruskal algorithm, launching several runs on a heterogeneous set of graphs and
different theoretical models for them.
