The constituents of many complex systems are characterized by non-trivial connectivity
patterns and dynamical processes that are well captured by network models. However, most …

A deluge of new data on real-world networks suggests that interactions among system units
are not limited to pairs, but often involve a higher number of nodes. To properly encode …

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of
available data. Traditionally, network models have provided key insights into the structure …

Graph-theoretical approaches have been widely applied in many disciplines, however, their
implementation in power electronics converters and systems is still in the exploring stage. In …

Network science is the field dedicated to the investigation and analysis of complex systems
via their representations as networks. We normally model such networks as graphs: sets of …

Network motifs are recurrent, small-scale patterns of interactions observed frequently in a
system. They shed light on the interplay between the topology and the dynamics of complex …

Many applications involve objects containing structure and rich content information, each
describing different feature aspects of the object. Graph learning and classification is a …

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 …

The consensus problem with asymmetric confidence intervals considered in this paper is
characterized by the fact that each agent can have optimistic and/or pessimistic interactions …

Many complex systems can be readily modeled as networks and represented as graphs.
Such systems include social interactions, transport infrastructures, biological pathways …