Networks are useful for representing phenomena in a broad range of domains. Although
their ability to represent complexity can be a virtue, it is sometimes useful to focus on a …

Recent empirical evidence has shown that in many real-world systems, successfully
represented as networks, interactions are not limited to dyads, but often involve three or …

Collective user behavior in social media applications often drives several important online
and offline phenomena linked to the spread of opinions and information. Several studies …

Logistic regression (LR) and support vector machine algorithms, together with linear and
nonlinear deep neural networks (DNNs), are applied to lending data in order to replicate …

Using a complex-network perspective, this paper empirically explores the determinants of
the process through which countries, given their capabilities, specialize in agricultural …

Contagion is an extremely important topic in finance. Contagion is at the core of most major
financial crises, in particular the global financial crisis that started in 2007. Although various …

Monopartite projections of bipartite networks are useful tools for modeling indirect
interactions in complex systems. The standard approach to identify significant links is …

Data-driven conceptual design is rapidly emerging as a powerful approach to generate
novel and meaningful ideas by leveraging external knowledge especially in the early design …

Networks are essential for analyzing complex systems. However, their growing size
necessitates backbone extraction techniques aimed at reducing their size while retaining …

In many data sets, information on the structure and temporality of a system coexists with
noise and non-essential elements. In networked systems for instance, some edges might be …