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Books > Professional & Technical > Energy technology & engineering > Electrical engineering
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Fast Uncovering of Graph Communities on a Chip - Toward Scalable Community Detection on Multicore and Manycore Platforms (Paperback)
Loot Price: R1,864
Discovery Miles 18 640
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Fast Uncovering of Graph Communities on a Chip - Toward Scalable Community Detection on Multicore and Manycore Platforms (Paperback)
Series: Foundations and Trends (R) in Electronic Design Automation
Expected to ship within 10 - 15 working days
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Graph representations are pervasive in scientific and social
computing. They serve as vital tools to model the interplay between
different interacting entities. This monograph delves into the
problem of community detection, which is one of the most widely
used graph operations toward scientific discovery. Community
detection refers to the process of identifying tightly-knit
subgroups of vertices in a large graph. These sub-groups (or
communities) represent vertices that are tied together through
common structure or function. Identification of communities could
help in understanding the modular organization of complex networks.
However, owing to large data sizes and high computational costs,
performing community detection at scale has become increasingly
challenging. This monograph presents a detailed review and analysis
of some of the leading computational methods and implementations
developed for executing community detection on modern day multicore
and manycore architectures. The intention is to: a) define the
problem of community detection and highlight its scientific
significance; b) relate to challenges in parallelizing the
operation on modern day architectures; c) provide a detailed report
and logical organization of the approaches that have been designed
for various architectures; and d) provide insights into the
strengths and suitability of different architectures for community
detection, and a preview into the future trends of the area. While
the focus is on community detection, the challenges, and techniques
to overcome the challenges, transcend to several other graph
problems that have applications in science and data analytics.
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