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Scalable Algorithms for Data and Network Analysis (Paperback)
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Scalable Algorithms for Data and Network Analysis (Paperback)
Series: Foundations and Trends (R) in Theoretical Computer Science
Expected to ship within 10 - 15 working days
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In the age of Big Data, efficient algorithms are in higher demand
more than ever before. While Big Data takes us into the asymptotic
world envisioned by our pioneers, it also challenges the classical
notion of efficient algorithms: algorithms that used to be
considered efficient, according to polynomial-time
characterization, may no longer be adequate for solving today's
problems. It is not just desirable but essential that efficient
algorithms should be scalable. In other words, their complexity
should be nearly linear or sub-linear with respect to the problem
size. Thus, scalability, not just polynomial-time computability,
should be elevated as the central complexity notion for
characterizing efficient computation. Scalable Algorithms for Data
and Network Analysis surveys a family of algorithmic techniques for
the design of scalable algorithms. These techniques include local
network exploration, advanced sampling, sparsification, and
geometric partitioning. They also include spectral
graph-theoretical methods, such as are used for computing
electrical flows and sampling from Gaussian Markov random fields.
These methods exemplify the fusion of combinatorial, numerical, and
statistical thinking in network analysis. This book illustrates the
use of these techniques by a few basic problems that are
fundamental in analyzing network data, particularly for the
identification of significant nodes and coherent
clusters/communities in social and information networks. It also
discusses some frameworks beyond graph-theoretical models for
studying conceptual questions that arise in network analysis and
social influences.
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