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Books > Computing & IT > Computer communications & networking
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Information and Influence Propagation in Social Networks (Paperback)
Loot Price: R1,118
Discovery Miles 11 180
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Information and Influence Propagation in Social Networks (Paperback)
Series: Synthesis Lectures on Data Management
Expected to ship within 10 - 15 working days
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Research on social networks has exploded over the last decade. To a
large extent, this has been fueled by the spectacular growth of
social media and online social networking sites, which continue
growing at a very fast pace, as well as by the increasing
availability of very large social network datasets for purposes of
research. A rich body of this research has been devoted to the
analysis of the propagation of information, influence, innovations,
infections, practices and customs through networks. Can we build
models to explain the way these propagations occur? How can we
validate our models against any available real datasets consisting
of a social network and propagation traces that occurred in the
past? These are just some questions studied by researchers in this
area. Information propagation models find applications in viral
marketing, outbreak detection, finding key blog posts to read in
order to catch important stories, finding leaders or trendsetters,
information feed ranking, etc. A number of algorithmic problems
arising in these applications have been abstracted and studied
extensively by researchers under the garb of influence
maximization. This book starts with a detailed description of
well-established diffusion models, including the independent
cascade model and the linear threshold model, that have been
successful at explaining propagation phenomena. We describe their
properties as well as numerous extensions to them, introducing
aspects such as competition, budget, and time-criticality, among
many others. We delve deep into the key problem of influence
maximization, which selects key individuals to activate in order to
influence a large fraction of a network. Influence maximization in
classic diffusion models including both the independent cascade and
the linear threshold models is computationally intractable, more
precisely #P-hard, and we describe several approximation algorithms
and scalable heuristics that have been proposed in the literature.
Finally, we also deal with key issues that need to be tackled in
order to turn this research into practice, such as learning the
strength with which individuals in a network influence each other,
as well as the practical aspects of this research including the
availability of datasets and software tools for facilitating
research. We conclude with a discussion of various research
problems that remain open, both from a technical perspective and
from the viewpoint of transferring the results of research into
industry strength applications.
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