For large-scale graph analytics on the GPU, the irregularity of data access and control flow,
and the complexity of programming GPUs, have presented two significant challenges to …

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 …

Exact triangle counting algorithm enumerates the triangles in a graph by identifying the
common neighbors of two vertices of each edge. In this work, we present TriCore, a scalable …

The rise of graph analytic systems has created a need for ways to measure and compare the
capabilities of these systems. Graph analytics present unique scalability difficulties. The …

Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching
for small patterns of interest. GPM applications are computationally expensive, and thus …

Triangle counting is an important problem in graph mining, which has achieved great
performance improvement on GPU in recent years. Instead of proposing a new GPU triangle …

In 2013, we released a position paper to launch a community effort to define a common set
of building blocks for constructing graph algorithms in the language of linear algebra. This …

Subgraph enumeration is important for many applications such as network motif discovery,
community detection, and frequent subgraph mining. To accelerate the execution, recent …

Triangle counting is a graph algorithm that calculates the number of triangles involving each
vertex in a graph. Briefly, a triangle encompasses three vertices from a graph, where every …

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 …