Random graph (RG) models play a central role in complex networks analysis. They help us
to understand, control, and predict phenomena occurring, for instance, in social networks …

Measuring the importance of nodes in a network with a centrality measure is an core task in
any network application. There many measures available and it is speculated that many …

The reproductive ratio, R 0, is a fundamental quantity in epidemiology, which determines the
initial increase in an infectious disease in a susceptible host population. In most epidemic …

Stochastic blockmodels are generative network models where the vertices are separated
into discrete groups, and the probability of an edge existing between two vertices is …

Intermediate-scale (or “meso-scale'') structures in networks have received considerable
attention, as the algorithmic detection of such structures makes it possible to discover …

We model network formation when heterogeneous nodes enter sequentially and form
connections through both random meetings and network-based search, but with type …

Recent research has highlighted the practical benefits of subjective interestingness
measures, which quantify the novelty or unexpectedness of a pattern when contrasted with …

Building on a recent effort that combines a first-principles approach to modeling router-level
connectivity with a more pragmatic use of statistics and graph theory, we show in this paper …

We study how the spread of computer viruses, worms, and other self-replicating malware is
affected by the logical topology of the network over which they propagate. We consider a …

The weaponization of digital communications and social media to conduct disinformation
campaigns at immense scale, speed, and reach presents new challenges to identify and …