  We present a novel method to reconstruct complex network from partial
information. We assume to know the links only for a subset of the nodes and to
know some non-topological quantity (fitness) characterising every node. The
missing links are generated on the basis of the latter quan- tity according to
a fitness model calibrated on the subset of nodes for which links are known. We
measure the quality of the reconstruction of several topological properties,
such as the network density and the degree distri- bution as a function of the
size of the initial subset of nodes. Moreover, we also study the resilience of
the network to distress propagation. We first test the method on ensembles of
synthetic networks generated with the Exponential Random Graph model which
allows to apply common tools from statistical mechanics. We then test it on the
empirical case of the World Trade Web. In both cases, we find that a subset of
10 % of nodes is enough to reconstruct the main features of the network along
with its resilience with an error of 5%.
