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Social Networks with Rich Edge Semantics (Paperback)
Loot Price: R1,461
Discovery Miles 14 610
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Social Networks with Rich Edge Semantics (Paperback)
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Expected to ship within 12 - 17 working days
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Social Networks with Rich Edge Semantics introduces a new mechanism
for representing social networks in which pairwise relationships
can be drawn from a range of realistic possibilities, including
different types of relationships, different strengths in the
directions of a pair, positive and negative relationships, and
relationships whose intensities change with time. For each
possibility, the book shows how to model the social network using
spectral embedding. It also shows how to compose the techniques so
that multiple edge semantics can be modeled together, and the
modeling techniques are then applied to a range of datasets.
Features Introduces the reader to difficulties with current social
network analysis, and the need for richer representations of
relationships among nodes, including accounting for intensity,
direction, type, positive/negative, and changing intensities over
time Presents a novel mechanism to allow social networks with
qualitatively different kinds of relationships to be described and
analyzed Includes extensions to the important technique of spectral
embedding, shows that they are mathematically well motivated and
proves that their results are appropriate Shows how to exploit
embeddings to understand structures within social networks,
including subgroups, positional significance, link or edge
prediction, consistency of role in different contexts, and net flow
of properties through a node Illustrates the use of the approach
for real-world problems for online social networks, criminal and
drug smuggling networks, and networks where the nodes are
themselves groups Suitable for researchers and students in social
network research, data science, statistical learning, and related
areas, this book will help to provide a deeper understanding of
real-world social networks.
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