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This book offers a holistic framework to study behavior and
evolutionary dynamics in large-scale, decentralized, and
heterogeneous crowd networks. In the emerging crowd
cyber-ecosystems, millions of deeply connected individuals, smart
devices, government agencies, and enterprises actively interact
with each other and influence each other's decisions. It is crucial
to understand such intelligent entities' behaviors and to study
their strategic interactions in order to provide important
guidelines on the design of reliable networks capable of predicting
and preventing detrimental events with negative impacts on our
society and economy. This book reviews the fundamental
methodologies to study user interactions and evolutionary dynamics
in crowd networks and discusses recent advances in this emerging
interdisciplinary research field. Using information diffusion over
social networks as an example, it presents a thorough investigation
of the impact of user behavior on the network evolution process and
demonstrates how this can help improve network performance.
Intended for graduate students and researchers from various
disciplines, including but not limited to, data science,
networking, signal processing, complex systems, and economics, the
book encourages researchers in related research fields to explore
the many untouched areas in this domain, and ultimately to design
crowd networks with efficient, effective, and reliable services.
This book offers a holistic framework to study behavior and
evolutionary dynamics in large-scale, decentralized, and
heterogeneous crowd networks. In the emerging crowd
cyber-ecosystems, millions of deeply connected individuals, smart
devices, government agencies, and enterprises actively interact
with each other and influence each other's decisions. It is crucial
to understand such intelligent entities' behaviors and to study
their strategic interactions in order to provide important
guidelines on the design of reliable networks capable of predicting
and preventing detrimental events with negative impacts on our
society and economy. This book reviews the fundamental
methodologies to study user interactions and evolutionary dynamics
in crowd networks and discusses recent advances in this emerging
interdisciplinary research field. Using information diffusion over
social networks as an example, it presents a thorough investigation
of the impact of user behavior on the network evolution process and
demonstrates how this can help improve network performance.
Intended for graduate students and researchers from various
disciplines, including but not limited to, data science,
networking, signal processing, complex systems, and economics, the
book encourages researchers in related research fields to explore
the many untouched areas in this domain, and ultimately to design
crowd networks with efficient, effective, and reliable services.
In large-scale media-sharing social networks, where millions of
users create, share, link and reuse media content, there are clear
challenges in protecting content security and intellectual
property, and in designing scalable and reliable networks capable
of handling high levels of traffic. This comprehensive resource
demonstrates how game theory can be used to model user dynamics and
optimize design of media-sharing networks. It reviews the
fundamental methodologies used to model and analyze human behavior,
using examples from real-world multimedia social networks. With a
thorough investigation of the impact of human factors on multimedia
system design, this accessible book shows how an understanding of
human behavior can be used to improve system performance. Bringing
together mathematical tools and engineering concepts with ideas
from sociology and human behavior analysis, this one-stop guide
will enable researchers to explore this emerging field further and
ultimately design media-sharing systems with more efficient, secure
and personalized services.
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