Big data has become an important issue for a large number of research areas such as data
mining, machine learning, computational intelligence, information fusion, the semantic Web …

The introduction of Google's Pregel generated much interest in the field of large-scale graph
data processing, inspiring the development of Pregel-like systems such as Apache Giraph …

Recently, increasingly large amounts of data are generated from a variety of sources.
Existing data processing technologies are not suitable to cope with the huge amounts of …

Existing graph-processing frameworks let users develop efficient implementations for many
graph problems, but none of them support efficiently bucketing vertices, which is needed for …

Massive graphs, such as online social networks and communication networks, have become
common today. To efficiently analyze such large graphs, many distributed graph computing …

In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph
analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic …

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

The Linked Data Benchmark Council's Social Network Benchmark (LDBC SNB) is an effort
intended to test various functionalities of systems used for graph-like data management. For …

Graph data management concerns the research and development of powerful technologies
for storing, processing and analyzing large volumes of graph data. This chapter presents an …

Graph processing engines following either the push-based or pull-based pattern
conceptually consist of a two-level nested loop structure. Parallelizing and vectorizing these …