em architectures to programming models. In this chapter, we review the challenges of parallel processing of large graphs, representative graph processing systems, general principles of designing …

GraphX (OSDI’14): layer over Spark for graph processing. Recasts graph-specific optimizations as distributed join optimizations and materialized view maintenance

Graph Signal Processing: Overview, Challenges, and Applications This article presents methods to process data associated to graphs (graph signals) extending techniques (transforms, sampling, and …

as developed for this purpose. Pregel is a distributed message-passing system, in which the vertices of the graph are distributed across compute nodes and send each other messa es to perform the …

Big Data: Graph Processing COS 418: Distributed Systems Lecture 21 Kyle Jamieson [Content adapted from J. Gonzalez]

It is one of the most critical modules in graph processing because more than 95% of computational throughput can be associated with the sorting of indices. The sparse matrix and graph operations …

In recent years, many CPU-GPU heterogeneous graph processing systems have been developed in both academic and industrial to facilitate large-scale graph processing in various applications, e.g., …