There has been significant recent interest in parallel frameworks for processing graphs due
to their applicability in studying social networks, the Web graph, networks in biology, and …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

GPS (for Graph Processing System) is a complete open-source system we developed for
scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large …

The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

Graph analysis performs many random reads and writes, thus, these workloads are typically
performed in memory. Traditionally, analyzing large graphs requires a cluster of machines …

We introduce NetworKit, an open-source software package for analyzing the structure of
large complex networks. Appropriate algorithmic solutions are required to handle …

We study compression techniques for parallel in-memory graph algorithms, and show that
we can achieve reduced space usage while obtaining competitive or improved performance …

Graph attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been
shown to be more powerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs …

We study the problem of implementing graph algorithms efficiently on Pregel-like systems,
which can be surprisingly challenging. Standard graph algorithms in this setting can incur …

Graph-processing platforms are increasingly used in a variety of domains. Although both
industry and academia are developing and tuning graph-processing algorithms and …