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Social media greatly enables people to participate in online
activities and shatters the barrier for online users to create and
share information at any place at any time. However, the explosion
of user-generated content poses novel challenges for online users
to find relevant information, or, in other words, exacerbates the
information overload problem. On the other hand, the quality of
user-generated content can vary dramatically from excellence to
abuse or spam, resulting in a problem of information credibility.
The study and understanding of trust can lead to an effective
approach to addressing both information overload and credibility
problems. Trust refers to a relationship between a trustor (the
subject that trusts a target entity) and a trustee (the entity that
is trusted). In the context of social media, trust provides
evidence about with whom we can trust to share information and from
whom we can accept information without additional verification.
With trust, we make the mental shortcut by directly seeking
information from trustees or trusted entities, which serves a
two-fold purpose: without being overwhelmed by excessive
information (i.e., mitigated information overload) and with
credible information due to the trust placed on the information
provider (i.e., increased information credibility). Therefore,
trust is crucial in helping social media users collect relevant and
reliable information, and trust in social media is a research topic
of increasing importance and of practical significance. This book
takes a computational perspective to offer an overview of
characteristics and elements of trust and illuminate a wide range
of computational tasks of trust. It introduces basic concepts,
deliberates challenges and opportunities, reviews state-of-the-art
algorithms, and elaborates effective evaluation methods in the
trust study. In particular, we illustrate properties and
representation models of trust, elucidate trust prediction with
representative algorithms, and demonstrate real-world applications
where trust is explicitly used. As a new dimension of the trust
study, we discuss the concept of distrust and its roles in trust
computing.
Deep learning on graphs has become one of the hottest topics in
machine learning. The book consists of four parts to best
accommodate our readers with diverse backgrounds and purposes of
reading. Part 1 introduces basic concepts of graphs and deep
learning; Part 2 discusses the most established methods from the
basic to advanced settings; Part 3 presents the most typical
applications including natural language processing, computer
vision, data mining, biochemistry and healthcare; and Part 4
describes advances of methods and applications that tend to be
important and promising for future research. The book is
self-contained, making it accessible to a broader range of readers
including (1) senior undergraduate and graduate students; (2)
practitioners and project managers who want to adopt graph neural
networks into their products and platforms; and (3) researchers
without a computer science background who want to use graph neural
networks to advance their disciplines.
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Catan
(16)
R1,150
R887
Discovery Miles 8 870
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