The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Multilayer networks is a rising topic in Network Science which characterizes the structure
and the function of complex systems formed by several interacting networks. Multilayer …

Google's PageRank method was developed to evaluate the importance of web-pages via
their link structure. The mathematics of PageRank, however, are entirely general and apply …

A network is a collection of nodes and edges, where an edge connects two nodes. Many
social, natural and engineered systems can be represented as networks. Examples include …

The network of interactions among fluid elements and coherent structures gives rise to the
incredibly rich dynamics of vortical flows. These interactions can be described with the use …

Numerous centrality measures have been developed to quantify the importances of nodes in
time-independent networks, and many of them can be expressed as the leading eigenvector …

I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few
methods and ideas, including several of personal interest, that I anticipate to be especially …

Centrality is one of the most studied concepts in network analysis. Despite an abundance of
methods for measuring centrality in social networks has been proposed, each approach …

It is clear that the topological structure of a neural network somehow determines the activity
of the neurons within it. In the present work, we ask to what extent it is possible to examine …

Characterizing the importances (ie, centralities) of nodes in social, biological, and
technological networks is a core topic in both network analysis and data science. We …