Physicists recently observed that realistic complex networks emerge as discrete samples
from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node …

Random graph models are frequently used as a controllable and versatile data source for
experimental campaigns in various research fields. Generating such data-sets at scale is a …

Analyzing massive complex networks yields promising insights about our everyday lives.
Building scalable algorithms to do so is a challenging task that requires a careful analysis …

Hyperbolic random graphs (HRGs) and geometric inhomogeneous random graphs (GIRGs)
are two similar generative network models that were designed to resemble complex real …

The most efficient algorithms for finding maximum independent sets in both theory and
practice use reduction rules to obtain a much smaller problem instance called a kernel. The …

A remarkable approach for grasping the relevant statistical features of real networks with the
help of random graphs is offered by hyperbolic models, centred around the idea of placing …

The study of vertex centrality measures is a key aspect of network analysis. Naturally, such
centrality measures have been generalized to groups of vertices; for popular measures it …

The key requirement to routing in any telecommunication network, and especially in Internet-
of-Things (IoT) networks, is scalability. Routing must route packets between any source and …

The abundance of massive network data in a plethora of applications makes scalable
analysis algorithms and software tools necessary to generate knowledge from such data in …

Several observations indicate the existence of a latent hyperbolic space behind real
networks that makes their structure very intuitive in the sense that the probability for a …