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 …

Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect
more than two vertices. They have a similarly wide range of applications as graphs. This …

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 …

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 …

Most of the current complex networks that are of interest to practitioners possess a certain
community structure that plays an important role in understanding the properties of these …

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks
is a frequently needed operation in processing graphs. Recently, size, variety, and structural …

Partitioning a graph into blocks of" roughly equal" weight while cutting only few edges is a
fundamental problem in computer science with a wide range of applications. In particular …

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous
methods have been developed to accelerate SpMV. However, no single method consistently …

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

Community detection in graphs is a canonical social network analysis method. We consider
the problem of generating suites of teras-cale synthetic social networks to compare the …