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Graph Sampling (Hardcover)
Loot Price: R1,493
Discovery Miles 14 930
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Graph Sampling (Hardcover)
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Many technological, socio-economic, environmental, biomedical
phenomena exhibit an underlying graph structure. Valued graph
allows one to incorporate the connections or links among the
population units in addition. The links may provide effectively
access to the part of population that is the primary target, which
is the case for many unconventional sampling methods, such as
indirect, network, line-intercept or adaptive cluster sampling. Or,
one may be interested in the structure of the connections, in terms
of the corresponding graph properties or parameters, such as when
various breadth- or depth-first non-exhaustive search algorithms
are applied to obtain compressed views of large often dynamic
graphs. Graph sampling provides a statistical approach to study
real graphs from either of these perspectives. It is based on
exploring the variation over all possible sample graphs (or
subgraphs) which can be taken from the given population graph, by
means of the relevant known sampling probabilities. The resulting
design-based inference is valid whatever the unknown properties of
the given real graphs. One-of-a-kind treatise of multidisciplinary
topics relevant to statistics, mathematics and data science.
Probabilistic treatment of breadth-first and depth-first
non-exhaustive search algorithms in graphs. Presenting cutting-edge
theory and methods based on latest research. Pathfinding for future
research on sampling from real graphs. Graph Sampling can primarily
be used as a resource for researchers working with sampling or
graph problems, and as the basis of an advanced course for
post-graduate students in statistics, mathematics and data science.
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