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Network science is a rapidly emerging field of study that
encompasses mathematics, computer science, physics, and
engineering. A key issue in the study of complex networks is to
understand the collective behavior of the various elements of these
networks. Although the results from graph theory have proven to be
powerful in investigating the structures of complex networks, few
books focus on the algorithmic aspects of complex network analysis.
Filling this need, Complex Networks: An Algorithmic Perspective
supplies the basic theoretical algorithmic and graph theoretic
knowledge needed by every researcher and student of complex
networks. This book is about specifying, classifying, designing,
and implementing mostly sequential and also parallel and
distributed algorithms that can be used to analyze the static
properties of complex networks. Providing a focused scope which
consists of graph theory and algorithms for complex networks, the
book identifies and describes a repertoire of algorithms that may
be useful for any complex network. Provides the basic background in
terms of graph theory Supplies a survey of the key algorithms for
the analysis of complex networks Presents case studies of complex
networks that illustrate the implementation of algorithms in
real-world networks, including protein interaction networks, social
networks, and computer networks Requiring only a basic discrete
mathematics and algorithms background, the book supplies guidance
that is accessible to beginning researchers and students with
little background in complex networks. To help beginners in the
field, most of the algorithms are provided in ready-to-be-executed
form. While not a primary textbook, the author has included
pedagogical features such as learning objectives, end-of-chapter
summaries, and review questions
Network science is a rapidly emerging field of study that
encompasses mathematics, computer science, physics, and
engineering. A key issue in the study of complex networks is to
understand the collective behavior of the various elements of these
networks. Although the results from graph theory have proven to be
powerful in investigating the structures of complex networks, few
books focus on the algorithmic aspects of complex network analysis.
Filling this need, Complex Networks: An Algorithmic Perspective
supplies the basic theoretical algorithmic and graph theoretic
knowledge needed by every researcher and student of complex
networks. This book is about specifying, classifying, designing,
and implementing mostly sequential and also parallel and
distributed algorithms that can be used to analyze the static
properties of complex networks. Providing a focused scope which
consists of graph theory and algorithms for complex networks, the
book identifies and describes a repertoire of algorithms that may
be useful for any complex network. Provides the basic background in
terms of graph theory Supplies a survey of the key algorithms for
the analysis of complex networks Presents case studies of complex
networks that illustrate the implementation of algorithms in
real-world networks, including protein interaction networks, social
networks, and computer networks Requiring only a basic discrete
mathematics and algorithms background, the book supplies guidance
that is accessible to beginning researchers and students with
little background in complex networks. To help beginners in the
field, most of the algorithms are provided in ready-to-be-executed
form. While not a primary textbook, the author has included
pedagogical features such as learning objectives, end-of-chapter
summaries, and review questions
This unique textbook/reference presents unified coverage of
bioinformatics topics relating to both biological sequences and
biological networks, providing an in-depth analysis of cutting-edge
distributed algorithms, as well as of relevant sequential
algorithms. In addition to introducing the latest algorithms in
this area, more than fifteen new distributed algorithms are also
proposed. Topics and features: reviews a range of open challenges
in biological sequences and networks; describes in detail both
sequential and parallel/distributed algorithms for each problem;
suggests approaches for distributed algorithms as possible
extensions to sequential algorithms, when the distributed
algorithms for the topic are scarce; proposes a number of new
distributed algorithms in each chapter, to serve as potential
starting points for further research; concludes each chapter with
self-test exercises, a summary of the key points, a comparison of
the algorithms described, and a literature review.
This unique textbook/reference presents unified coverage of
bioinformatics topics relating to both biological sequences and
biological networks, providing an in-depth analysis of cutting-edge
distributed algorithms, as well as of relevant sequential
algorithms. In addition to introducing the latest algorithms in
this area, more than fifteen new distributed algorithms are also
proposed. Topics and features: reviews a range of open challenges
in biological sequences and networks; describes in detail both
sequential and parallel/distributed algorithms for each problem;
suggests approaches for distributed algorithms as possible
extensions to sequential algorithms, when the distributed
algorithms for the topic are scarce; proposes a number of new
distributed algorithms in each chapter, to serve as potential
starting points for further research; concludes each chapter with
self-test exercises, a summary of the key points, a comparison of
the algorithms described, and a literature review.
This book presents a comprehensive review of key distributed graph
algorithms for computer network applications, with a particular
emphasis on practical implementation. Topics and features:
introduces a range of fundamental graph algorithms, covering
spanning trees, graph traversal algorithms, routing algorithms, and
self-stabilization; reviews graph-theoretical distributed
approximation algorithms with applications in ad hoc wireless
networks; describes in detail the implementation of each algorithm,
with extensive use of supporting examples, and discusses their
concrete network applications; examines key graph-theoretical
algorithm concepts, such as dominating sets, and parameters for
mobility and energy levels of nodes in wireless ad hoc networks,
and provides a contemporary survey of each topic; presents a simple
simulator, developed to run distributed algorithms; provides
practical exercises at the end of each chapter.
This book presents a comprehensive review of key distributed graph
algorithms for computer network applications, with a particular
emphasis on practical implementation. Topics and features:
introduces a range of fundamental graph algorithms, covering
spanning trees, graph traversal algorithms, routing algorithms, and
self-stabilization; reviews graph-theoretical distributed
approximation algorithms with applications in ad hoc wireless
networks; describes in detail the implementation of each algorithm,
with extensive use of supporting examples, and discusses their
concrete network applications; examines key graph-theoretical
algorithm concepts, such as dominating sets, and parameters for
mobility and energy levels of nodes in wireless ad hoc networks,
and provides a contemporary survey of each topic; presents a simple
simulator, developed to run distributed algorithms; provides
practical exercises at the end of each chapter.
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