Graph analytics, which mainly includes graph processing, graph mining, and graph learning,
has become increasingly important in several domains, including social network analysis …

Graph computing is widely applied in a large number of big data applications. Despite its
importance, high performance graph computing remains a challenge, especially for large …

Graph processing is an important analysis technique for a wide range of big data
applications. The ability to explicitly represent relationships between entities gives graph …

With the emergence of data science, graph computing is becoming a crucial tool for
processing big connected data. Although efficient implementations of specific graph …

Graph analytics systems have gained significant popularity due to the prevalence of graph
data. Many of these systems are designed to run in a shared-nothing architecture whereby a …

In recent years we have witnessed a surging interest in developing Big Graph processing
systems. To date, tens of Big Graph systems have been proposed. This tutorial provides a …

Graph is a fundamental data structure that captures relationships between different data
entities. In practice, graphs are widely used for modeling complicated data in different …

Graph algorithms are becoming increasingly important for analyzing large datasets in many
fields. Real-world graph data follows a pattern of sparsity, that is not uniform but highly …

Emerging applications in health-care, social media analytics, cyber-security, homeland
security, and marketing require large graph analytics. Attaining good performance on these …

Graph processing has been widely used in many scenarios, from scientific computing to
artificial intelligence. Graph processing exhibits irregular computational parallelism and …