Community detection is a common problem in graph data analytics that consists of finding
groups of densely connected nodes with few connections to nodes outside of the group. In …

The GraphBLAS standard (GraphBlas. org) is being developed to bring the potential of
matrix-based graph algorithms to the broadest possible audience. Mathematically, the …

Community detection has become a fundamental operation in numerous graph-theoretic
applications. It is used to reveal natural divisions that exist within real world networks without …

In this paper we present and evaluate a parallel community detection algorithm derived from
the state-of-the-art Louvain modularity maximization method. Our algorithm adopts a novel …

This paper reports on methods and results of an applied research project by a team
consisting of SAIC and four universities to develop, integrate, and evaluate new approaches …

Community detection is a common problem that exists in the data graph analytics as the
social networks analytics. In the context of social networks, community detection is aimed at …

Graph-processing platforms are increasingly used in a variety of domains. Although both
industry and academia are develo** and tuning graph-processing algorithms and …

Graph clustering and community detection are central problems in modern data mining. The
increasing need for analyzing billion-scale data calls for faster and more scalable algorithms …

The analysis of graphs has become increasingly important to a wide range of applications.
Graph analysis presents a number of unique challenges in the areas of (1) software …

Graph clustering, popularly known as community detection, is a fundamental kernel for
several applications of relevance to the Defense Advanced Research Projects Agency's …