  Bipartite networks provide an insightful representation of many systems,
ranging from mutualistic networks of species interactions to investment
networks in finance. The analysis of their topological structures has revealed
the ubiquitous presence of properties which seem to characterize many -
apparently different - systems. Nestedness, for example, has been observed in
plants-pollinator as well as in country-product trade networks. This has raised
questions about the significance of these patterns, which are often believed to
constitute a genuine signature of self-organization. Here, we review several
methods that have been developed for the analysis of such evidence. Due to the
interdisciplinary character of complex networks, tools developed in one field,
for example ecology, can greatly enrich other areas of research, such as
economy and finance, and vice versa. With this in mind, we briefly review
several entropy-based bipartite null models that have been recently proposed
and discuss their application to several real-world systems. The focus on these
models is motivated by the fact that they show three very desirable features:
analytical character, general applicability and versatility. In this respect,
entropy-based methods have been proven to perform satisfactorily both in
providing benchmarks for testing evidence-based null hypotheses and in
reconstructing unknown network configurations from partial information. On top
of that, entropy-based models have been successfully employed to analyze
ecological as well as economic systems, thus representing an ideal,
interdisciplinary tool to approach the study of bipartite complex systems.
[...]
