Triangle Counting (TC) is a basic graph mining problem with numerous applications.
However, the large size of real-world graphs has a severe effect on TC performance. This …

Graph algorithms (eg, triangle counting) are widely used to find the deep association of data
in various real-world applications such as friend recommendation and junk mail detection …

The notion of “centrality” within graph analytics has led to the creation of well-known metrics
such as Google's Page Rank [1], which is an extension of eigenvector centrality [2]. Triangle …

Listing and counting triangles in graphs is a key algorithmic kernel for network analyses
including community detection, clustering coefficients, k-trusses, and triangle centrality. We …

Graph algorithms find several usages in industry, science, humanities, and technology. The
fast-growing size of graph datasets in the context of the processing model of the current …

The transitive closure of a graph is a new graph where every vertex is directly connected to
all vertices to which it had a path in the original graph. Transitive closures are useful for …

Even though subgraph counting and subgraph matching are well-known NP-Hard problems,
they are foundational building blocks for many scientific and commercial applications. In …

Triangle counting is a cornerstone operation used in large graph analytics. Hash-based
algorithms for triangle counting fail to take advantage of available vectorization in modern …

Triangles are an essential part of network analysis, representing metrics such as transitivity
ratio and clustering coefficient Because of its diverse applications, enumeration and …

Driven by the need to find alternative accelerators which can viably replace graphics
processing units (GPUs) in next-generation Supercomputing systems, this paper proposes a …