@mymaxsam] that most of the algorithms developed to randomize a real-world network suffer from severe limitations and give biased results. To overcome these limitations, we use a recently proposed unbiased method [@mastrandrea2013enhanced] based on the maximum-likelihood estimation [@mylikelihood] of maximum-entropy ensembles of graphs [@Squartini_Garlaschelli_2011]. One of the attractive features of this method is its fully analytical character, which allows us to obtain the exact expressions for the expected values without performing explicit averages over numerically sampled networks of the ensemble. Recently, a fast and unbiased algorithm has been released to computationally implement this procedure [@mymaxsam]. We briefly recall the main steps of this approach and of the enhanced network reconstruction method that, building on some general theoretical results [@mybosefermi], can be derived from it.

Firstly, we need to specify a set of constraints $\{C_i\}$. The constraints are the network properties that we want to preserve during the randomization procedure, according to the specific network and research question. Generally these constraints are *local*, such as the strength sequence defining the WCM, but the methodology can also account for non-local constraints in some cases [@squartini2012reciprocity; @picciolo2012role]. In order to construct the ECM, defined an ensemble of weighted networks where both the degree 