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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.
Logic and object-orientation have come to be recognized as being
among the most powerful paradigms for modeling information systems.
The term "information systems" is used here in a very general
context to denote database systems, software development systems,
knowledge base systems, proof support systems, distributed systems
and reactive systems. One of the most vigorously researched topics
common to all information systems is "formal modeling." An elegant
high-level abstraction applicable to both application domain and
system domain concepts will always lead to a system design from
"outside in"; that is, the aggregation of ideas is around real-life
objects about which the system is to be designed. Formal methods
\yhen applied with this view in mind, especially during early
stages of system development, can lead to a formal reasoning on the
intended properties, thus revealing system flaws that might
otherwise be discovered much later. Logic in different styles and
semantics is being used to model databases and their transactions;
it is also used to specify concurrent, distributed, real-time, and
reactive systems., The notion of "object" is central to the
modeling of object oriented databases, as well as object-oriented
design and programs in software engineering. Both database and
software engineering communities have undoubtedly made important
contributions to formalisms based on logic and objects. It is
worthwhile bringing together the ideas developed by the two
communities in isolation, and focusing on integrating their common
strengths."
Communities serve as basic structural building blocks for
understanding the organization of many real-world networks,
including social, biological, collaboration, and communication
networks. Recently, community search over graphs has attracted
significantly increasing attention, from small, simple, and static
graphs to big, evolving, attributed, and location-based graphs. In
this book, we first review the basic concepts of networks,
communities, and various kinds of dense subgraph models. We then
survey the state of the art in community search techniques on
various kinds of networks across different application areas.
Specifically, we discuss cohesive community search, attributed
community search, social circle discovery, and geo-social group
search. We highlight the challenges posed by different community
search problems. We present their motivations, principles,
methodologies, algorithms, and applications, and provide a
comprehensive comparison of the existing techniques. This book
finally concludes by listing publicly available real-world datasets
and useful tools for facilitating further research, and by offering
further readings and future directions of research in this
important and growing area.
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