Distributed Graph Algorithms for Computer Networks

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.

Guide to Graph Algorithms

This clearly structured textbook/reference presents a detailed and comprehensive review of the fundamental principles of sequential graph algorithms, approaches for NP-hard graph problems, and approximation algorithms and heuristics for such problems. The work also provides a comparative analysis of sequential, parallel and distributed graph algorithms – including algorithms for big data – and an investigation into the conversion principles between the three algorithmic methods. Topics and features: presents a comprehensive analysis of sequential graph algorithms; offers a unifying view by examining the same graph problem from each of the three paradigms of sequential, parallel and distributed algorithms; describes methods for the conversion between sequential, parallel and distributed graph algorithms; surveys methods for the analysis of large graphs and complex network applications; includes full implementation details for the problems presented throughout the text; provides additional supporting material at an accompanying website. This practical guide to the design and analysis of graph algorithms is ideal for advanced and graduate students of computer science, electrical and electronic engineering, and bioinformatics. The material covered will also be of value to any researcher familiar with the basics of discrete mathematics, graph theory and algorithms.

Improved Distributed Algorithms for Fundamental Graph Problems

Distributed graph algorithms provide efficient and theoretically sound methods for solving graph problems in distributed settings and more generally for performing distributed computation in networks. These algorithms are applicable in a wide variety of settings, ranging from computer networks to massively parallel computing and beyond. This thesis addresses a number of the central problems of distributed graph algorithms. These problems generally revolve around two of the principal challenges of the area, locality and congestion. The problems include computing maximal independent set, minimum spanning tree, minimum edge cut and minimum vertex cut, graph connectivity decompositions, network information dissemination, minimum-weight connected dominating set, and scheduling distributed protocols. We develop novel techniques, concepts, and tools for these problems, and present algorithms and impossibility results which improve considerably on the state of the art, in several cases resolving or advancing long-standing open problems.

Algebraic Graph Algorithms

This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and/or graph algorithms.

Advanced Methods for Complex Network Analysis

As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Advanced Methods for Complex Network Analysis features the latest research on the algorithms and analysis measures being employed in the field of network science. Highlighting the application of graph models, advanced computation, and analytical procedures, this publication is a pivotal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.

The Practitioner's Guide to Graph Data

Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system

Swarm Intelligence

This book constitutes the proceedings of the 9th International Conference on Swarm Intelligence, held in Brussels, Belgium, in September 2014. This volume contains 17 full papers, 9 short papers, and 7 extended abstracts carefully selected out of 55 submissions. The papers cover empirical and theoretical research in swarm intelligence such as: behavioral models of social insects or other animal societies, ant colony optimization, particle swarm optimization, swarm robotics systems.

Distributed and Sequential Algorithms for Bioinformatics

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.

Complex Networks

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

Discrete Mathematics and Graph Theory

This textbook can serve as a comprehensive manual of discrete mathematics and graph theory for non-Computer Science majors; as a reference and study aid for professionals and researchers who have not taken any discrete math course before. It can also be used as a reference book for a course on Discrete Mathematics in Computer Science or Mathematics curricula. The study of discrete mathematics is one of the first courses on curricula in various disciplines such as Computer Science, Mathematics and Engineering education practices. Graphs are key data structures used to represent networks, chemical structures, games etc. and are increasingly used more in various applications such as bioinformatics and the Internet. Graph theory has gone through an unprecedented growth in the last few decades both in terms of theory and implementations; hence it deserves a thorough treatment which is not adequately found in any other contemporary books on discrete mathematics, whereas about 40% of this textbook is devoted to graph theory. The text follows an algorithmic approach for discrete mathematics and graph problems where applicable, to reinforce learning and to show how to implement the concepts in real-world applications.