  In the past years statistical physics has been successfully applied for
complex networks modelling. In particular, it has been shown that the maximum
entropy principle can be exploited in order to construct graph ensembles for
real-world networks which maximize the randomness of the graph structure
keeping fixed some topological constraint. Such ensembles can be used as null
models to detect statistically significant structural patterns and to
reconstruct the network structure in cases of incomplete information. Recently,
these randomizing methods have been used for the study of self-organizing
systems in economics and finance, such as interbank and world trade networks,
in order to detect topological changes and, possibly, early-warning signals for
the economical crisis. In this work we consider the configuration models with
different constraints for the network of mergers and acquisitions (M&As),
Comparing triadic and dyadic motifs, for both the binary and weighted M&A
network, with the randomized counterparts can shed light on its organization at
higher order level.
