High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …

Graph pattern mining (GPM) is a class of algorithms widely used in many real-world
applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a …

Graph pattern mining (GPM) is a key building block in diverse applications, including
bioinformatics, chemical engineering, social network analysis, recommender systems and …

Graph mining is an emerging application of high importance and also with high complexity,
thus requiring efficient hardware acceleration. Current accelerator designs only utilize …

Graph mining is an emerging application of great importance to big data analytic. Graph
mining algorithms are bottle-necked by both computation complexity and memory access …

Counting triangles in a graph and incident to each vertex is a fundamental and frequently
considered task of graph analysis. We consider how to efficiently do this for huge graphs …

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

Data from emerging applications, such as cybersecurity and social networking, can be
abstracted as graphs whose edges are updated sequentially in the form of a stream. The …

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 …

" Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over
the graph or by exploring the structure of the graph. What could be easier? Turns out …