The study of epidemics is useful for not only understanding outbreaks and trying to limit their
adverse effects, but also because epidemics are related to social phenomena such as …

The rapid growth in the volume of many real-world graphs (eg, social networks, web graphs,
and spatial networks) has led to the development of various vertex-centric distributed graph …

With the prevalence of graph data in real-world applications (eg, social networks, mobile
phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph …

Core decomposition has been applied widely in the visualization and analysis of massive
networks. However, existing studies of core decomposition were only limited to non …

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 …

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 …

Many real-world interactions (eg, researcher collaborations and email communication) occur
among multiple entities. These group interactions are naturally modeled as hypergraphs. In …

Influence Maximization aims to find the top-(K) influential individuals to maximize the
influence spread within a social network, which remains an important yet challenging …

Synthetic power graphs have recently been widely used to assist researchers in analyzing
power grids as complex networks. In general, power graphs are sparsely connected, scale …

Real-world graphs are not always publicly available or sometimes do not meet specific
research requirements. These challenges call for generating synthetic networks that follow …