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We present a novel method to reconstruct complex network from partialinformation. We assume to know the links only for a subset of the nodes and toknow some non-topological quantity (fitness) characterising every node. Themissing links are generated on the basis of the latter quan- tity according toa fitness model calibrated on the subset of nodes for which links are known. Wemeasure the quality of the reconstruction of several topological properties,such as the network density and the degree distri- bution as a function of thesize of the initial subset of nodes. Moreover, we also study the resilience ofthe network to distress propagation. We first test the method on ensembles ofsynthetic networks generated with the Exponential Random Graph model whichallows to apply common tools from statistical mechanics. We then test it on theempirical case of the World Trade Web. In both cases, we find that a subset of10 % of nodes is enough to reconstruct the main features of the network alongwith its resilience with an error of 5%.

Bootstrapping topology and systemic risk of complex network using the fitness model