  The Cluster Variation Method known in statistical mechanics and condensed
matter is revived for weighted bipartite networks. The decomposition of a
Hamiltonian through a finite number of components, whence serving to define
variable clusters, is recalled. As an illustration the network built from data
representing correlations between (4) macro-economic features, i.e. the so
called $vector$ $components$, of 15 EU countries, as (function) nodes, is
discussed. We show that statistical physics principles, like the maximum
entropy criterion points to clusters, here in a (4) variable phase space: Gross
Domestic Product (GDP), Final Consumption Expenditure (FCE), Gross Capital
Formation (GCF) and Net Exports (NEX). It is observed that the $maximum$
entropy corresponds to a cluster which does $not$ explicitly include the GDP
but only the other (3) ''axes'', i.e. consumption, investment and trade
components. On the other hand, the $minimal$ entropy clustering scheme is
obtained from a coupling necessarily including GDP and FCE. The results confirm
intuitive economic theory and practice expectations at least as regards
geographical connexions. The technique can of course be applied to many other
cases in the physics of socio-economy networks.
