In the past 15 years, statistical physics has been successful as a framework for modelling
complex networks. On the theoretical side, this approach has unveiled a variety of physical …

The study of social, economic and biological systems is often (when not always) limited by
the partial information about the structure of the underlying networks. An example of …

The structure and dynamics of complex network systems are of current research interest. We
illustrate the dependency between the network topology and its function, considering the …

Complex networks datasets often come with the problem of missing information: interactions
data that have not been measured or discovered, may be affected by errors, or are simply …

In bipartite networks, community structures are restricted to being disassortative, in that
nodes of one type are grouped according to common patterns of connection with nodes of …

Financial networks are an important source of systemic risk, but often only partial network
information is available. In this paper, we use data on bank-firm credit relationships in Japan …

We define bipartite and monopartite relational networks of chemical elements and
compounds using two different datasets of inorganic chemical and material compounds, as …

Rectangular association matrices with binary (0/1) entries are a common data structure in
many research fields. Examples include ecology, economics, mathematics, physics …

Securities Holdings Statistics (SHS), compiled by the European System of Central Banks
(ESCB) have spurred research over the past decade. SHS provide high-quality security-by …

The aftermath of the recent financial crisis has shown how expensive and unfair the
stabilization of financial ecosystems can be. The main cause is the level of complexity of …