Triangles are the basic substructure of networks and triangle counting (TC) has been a
fundamental graph computing problem in numerous fields such as social network analysis …

Triangle counting is a building block for a wide range of graph applications. Traditional
wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle …

Graph processing has attracted much attention recently due to its popularity in many big
data analytic applications. With high performance and energy efficiency, FPGAs can be an …

We describe a novel multi-machine multi-GPU implementation of triangle counting which
exploits a novel application-agnostic graph partitioning strategy that eliminates almost all …

Graph analysis is a critical task in many fields, such as social networking, epidemiology,
bioinformatics, and fraud de-tection. In particular, understanding and inferring relationships …

Graph analytics are an emerging class of irregular applications. Operating on very large
datasets, they present unique behaviors, such as fine-grained, unpredictable memory …

Understanding the higher-order interactions within network data is a key objective of
network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched …

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 real-world entities and their relationships can be modeled using temporal graphs,
where each edge is associated with a timestamp denoting the time of interaction between …

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 …