Triangle count and local clustering coefficient are two core metrics for graph analysis. They
find broad application in analyses such as community detection and link recommendation …

This paper considers structures of systems beyond dyadic (pairwise) interactions and
investigates mathematical modeling of multi-way interactions and connections as hyper …

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in
various combinatorial and graph algorithms as well as various bioinformatics and data …

The C++ Standard Library is a valuable collection of generic algorithms and data structures
that improves the usability and reliability of C++ software. Graph algorithms and data …

Counting instances of specific subgraphs in a larger graph is an important problem in graph
mining. Finding cliques of size k (k-cliques) is one example of this NP-hard problem …

This paper considers structures of systems beyond dyadic (pairwise) interactions and
investigates mathematical modeling of multi-way interactions and connections as …

Most big data processing tasks today rely heavily on graph and tensor algorithms to uncover
useful information within real-world data. Graph algorithms are used to model relationships …

Modern problem sizes very often exceed the capabilities of homogenous single-node
systems. To overcome this problem, developers use multi-node systems with heterogeneous …

Increased chip densities offer massive computation power to deal with fundamental big data
operations such as sorting. At the same time the proliferation of processing elements (PEs) …

Understanding relationships among entities in large-scale systems is a fundamental
operation in data science. Such systems can be modeled as graphs if the relationships are …