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Graph clustering, popularly known as community detection, is a fundamental kernel for several applications of relevance to the Defense Advanced Research Projects Agency's (DARPA) Hierarchical Identify Verify Exploit (HIVE) Program. Clusters or communities represent natural divisions within a network that are densely connected within a cluster and sparsely connected to the rest of the network. The...
With its applicability spanning numerous data-driven fields, the implementation of graph analytics on multicore platforms is gaining momentum. The most important component of a multicore chip is its communication backbone. Due to the inherent irregularities in data movements manifested by graph based applications, it is essential to design an efficient on-chip interconnect for multicore chips performing...
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 imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to...
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