Social Networks With Rich Edge Semantics (chapman & Hall/crc Data Mining And Knowledge Discovery Series)
by David Skillicorn /
2017 / English / PDF
3.7 MB Download
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.
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
FeaturesIntroduces 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
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
timePresents a novel mechanism to allow social networks with
qualitatively different kinds of relationships to be described
and analyzed
Presents a novel mechanism to allow social networks with
qualitatively different kinds of relationships to be described
and analyzedIncludes extensions to the important technique of spectral
embedding, shows that they are mathematically well motivated and
proves that their results are appropriate
Includes extensions to the important technique of spectral
embedding, shows that they are mathematically well motivated and
proves that their results are appropriateShows 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
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 nodeIllustrates 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
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.
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.
