The rise of graph analytic systems has created a need for new ways to measure and
compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph …

The rise of graph analytic systems has created a need for new ways to measure and
compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph …

Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes,
new tools must be developed to enable data scientists to handle such graphs efficiently …

Graph algorithms are increasingly used in applications that exploit large databases.
However, conventional processor architectures are inadequate for handling the throughput …

Triangle counting is a representative graph analysis algorithm with several applications. It is
also one of the three benchmarks used in the IEEE HPEC Graph Challenge. Triangle …

Graphs are ubiquitous in modeling complex systems and representing interactions between
entities to uncover structural information of the domain. Traditionally, graph analytics …

Triangle counting serves as a key building block for a set of important graph algorithms in
network science. In this paper, we address the IEEE HPEC Static Graph Challenge problem …

Graph analytics has emerged as an important tool in the analysis of large scale data from
diverse application domains such as social networks, cyber security and bioinformatics …

The widespread use of graphs to model large scale real-world data brings with it the need
for fast graph analytics. In this paper, we explore the problem of triangle counting, 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 …