Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Graph processing workloads are memory intensive with irregular access patterns and large
memory footprint resulting in low data locality. Their popular software implementations …

Graph Processing is at the core of many critical emerging workloads operating on
unstructured data, including social network analysis, bioinformatics, and many others. Many …

This paper studies incremental graph partitioning. Given a (vertex-cut or edge-cut) partition
C (G) of a graph G and updates ΔG to G, it is to compute changes ΔO to C (G), yielding a …

Making sense of large graph datasets is a fundamental and challenging process that
advances science, education and technology. We survey research on graph exploration and …

Graphs are a tremendously suitable data representations that model the relationships of
entities in many application domains, such as recommendation systems, machine learning …

We introduce XtraPuLP, a new distributed-memory graph partitioner designed to process
trillion-edge graphs. XtraPuLP is based on the scalable label propagation community …

Current mobile devices are often equipped with several network interfaces, which may be of
different access technologies, both wireless and cellular. Different requirements of different …

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 …