  We present a new approach to the calculation of measures in weighted
networks, based on the translation of a weighted network into an ensemble of
edges. This leads to a straightforward generalization of any measure defined on
unweighted networks, such as the average degree of the nearest neighbours, the
clustering coefficient, the `betweenness', the distance between two nodes and
the diameter of a network. All these measures are well established for
unweighted networks but have hitherto proven difficult to define for weighted
networks. Further to introducing this approach we demonstrate its advantages by
applying the clustering coefficient constructed in this way to two real-world
weighted networks.
