Computational Network Science

Computational Network Science
Author: Henry Hexmoor
Publsiher: Morgan Kaufmann
Total Pages: 128
Release: 2014-09-23
ISBN 10: 0128011564
ISBN 13: 9780128011560
Language: EN, FR, DE, ES & NL

Computational Network Science Book Review:

The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline. Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research. Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science Comprehensive coverage of Network Science algorithms, methodologies, and common problems Includes references to formative and updated developments in the field Coverage spans mathematical sociology, economics, political science, and biological networks

Computational Network Analysis with R

Computational Network Analysis with R
Author: Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib
Publsiher: John Wiley & Sons
Total Pages: 368
Release: 2016-12-12
ISBN 10: 3527339582
ISBN 13: 9783527339587
Language: EN, FR, DE, ES & NL

Computational Network Analysis with R Book Review:

This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

A First Course in Network Science

A First Course in Network Science
Author: Filippo Menczer,Santo Fortunato,Clayton A. Davis
Publsiher: Cambridge University Press
Total Pages: 300
Release: 2020-01-31
ISBN 10: 1108471137
ISBN 13: 9781108471138
Language: EN, FR, DE, ES & NL

A First Course in Network Science Book Review:

A practical introduction to network science for students across business, cognitive science, neuroscience, sociology, biology, engineering and other disciplines.

Computational Network Theory

Computational Network Theory
Author: Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl
Publsiher: John Wiley & Sons
Total Pages: 280
Release: 2015-11-02
ISBN 10: 3527337245
ISBN 13: 9783527337248
Language: EN, FR, DE, ES & NL

Computational Network Theory Book Review:

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are important tools to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks and special aspects of complex network analysis and operationsresearch.

Analyzing Network Data in Biology and Medicine

Analyzing Network Data in Biology and Medicine
Author: Nata a Pr ulj,Nataša Pržulj
Publsiher: Cambridge University Press
Total Pages: 672
Release: 2019-03-28
ISBN 10: 1108432239
ISBN 13: 9781108432238
Language: EN, FR, DE, ES & NL

Analyzing Network Data in Biology and Medicine Book Review:

Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.

Network Science

Network Science
Author: Albert-László Barabási
Publsiher: Cambridge University Press
Total Pages: 475
Release: 2016-07-21
ISBN 10: 1107076269
ISBN 13: 9781107076266
Language: EN, FR, DE, ES & NL

Network Science Book Review:

Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science.

Mining Complex Networks

Mining Complex Networks
Author: Bogumil Kaminski,Pawel Prałat,Francois Theberge
Publsiher: CRC Press
Total Pages: 278
Release: 2021-12-14
ISBN 10: 1000515907
ISBN 13: 9781000515909
Language: EN, FR, DE, ES & NL

Mining Complex Networks Book Review:

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product). This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumił Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumił is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem. Paweł Prałat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators. François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.

Network Science

Network Science
Author: Ernesto Estrada,Maria Fox,Desmond J. Higham,Gian-Luca Oppo
Publsiher: Springer Science & Business Media
Total Pages: 245
Release: 2010-08-24
ISBN 10: 1849963967
ISBN 13: 9781849963961
Language: EN, FR, DE, ES & NL

Network Science Book Review:

Network Science is the emerging field concerned with the study of large, realistic networks. This interdisciplinary endeavor, focusing on the patterns of interactions that arise between individual components of natural and engineered systems, has been applied to data sets from activities as diverse as high-throughput biological experiments, online trading information, smart-meter utility supplies, and pervasive telecommunications and surveillance technologies. This unique text/reference provides a fascinating insight into the state of the art in network science, highlighting the commonality across very different areas of application and the ways in which each area can be advanced by injecting ideas and techniques from another. The book includes contributions from an international selection of experts, providing viewpoints from a broad range of disciplines. It emphasizes networks that arise in nature—such as food webs, protein interactions, gene expression, and neural connections—and in technology—such as finance, airline transport, urban development and global trade. Topics and Features: begins with a clear overview chapter to introduce this interdisciplinary field; discusses the classic network science of fixed connectivity structures, including empirical studies, mathematical models and computational algorithms; examines time-dependent processes that take place over networks, covering topics such as synchronisation, and message passing algorithms; investigates time-evolving networks, such as the World Wide Web and shifts in topological properties (connectivity, spectrum, percolation); explores applications of complex networks in the physical and engineering sciences, looking ahead to new developments in the field. Researchers and professionals from disciplines as varied as computer science, mathematics, engineering, physics, chemistry, biology, ecology, neuroscience, epidemiology, and the social sciences will all benefit from this topical and broad overview of current activities and grand challenges in the unfolding field of network science.

Computational Network Science

Computational Network Science
Author: Henry Hexmoor
Publsiher: Morgan Kaufmann
Total Pages: 118
Release: 2014-09-29
ISBN 10: 9780128008911
ISBN 13: 0128008911
Language: EN, FR, DE, ES & NL

Computational Network Science Book Review:

The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline. Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research. Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science Comprehensive coverage of Network Science algorithms, methodologies, and common problems Includes references to formative and updated developments in the field Coverage spans mathematical sociology, economics, political science, and biological networks

Introduction to Computational Social Science

Introduction to Computational Social Science
Author: Claudio Cioffi-Revilla
Publsiher: Springer
Total Pages: 607
Release: 2017-06-29
ISBN 10: 3319501313
ISBN 13: 9783319501314
Language: EN, FR, DE, ES & NL

Introduction to Computational Social Science Book Review:

This textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.

Modern and Interdisciplinary Problems in Network Science

Modern and Interdisciplinary Problems in Network Science
Author: Zengqiang Chen,Matthias Dehmer,Frank Emmert-Streib,Yongtang Shi
Publsiher: CRC Press
Total Pages: 290
Release: 2020-09-30
ISBN 10: 9780367657062
ISBN 13: 0367657066
Language: EN, FR, DE, ES & NL

Modern and Interdisciplinary Problems in Network Science Book Review:

Modern and Interdisciplinary Problems in Network Science: A Translational Research Perspective covers a broad range of concepts and methods, with a strong emphasis on interdisciplinarity. The topics range from analyzing mathematical properties of network-based methods to applying them to application areas. By covering this broad range of topics, the book aims to fill a gap in the contemporary literature in disciplines such as physics, applied mathematics and information sciences.

Mining Lurkers in Online Social Networks

Mining Lurkers in Online Social Networks
Author: Andrea Tagarelli,Roberto Interdonato
Publsiher: Springer
Total Pages: 93
Release: 2018-11-09
ISBN 10: 3030002292
ISBN 13: 9783030002299
Language: EN, FR, DE, ES & NL

Mining Lurkers in Online Social Networks Book Review:

This SpringerBrief brings order to the wealth of research studies that contribute to shape our understanding of on-line social networks (OSNs) lurking phenomena. This brief also drives the development of computational approaches that can be effectively applied to answer questions related to lurking behaviors, as well as to the engagement of lurkers in OSNs. All large-scale online social networks (OSNs) are characterized by a participation inequality principle, i.e., the crowd of an OSN does not actively contribute, rather it takes on a silent role. Silent users are also referred to as lurkers, since they gain benefit from others' information without significantly giving back to the community. Nevertheless, lurkers acquire knowledge from the OSN, therefore a major goal is to encourage them to more actively participate. Lurking behavior analysis has been long studied in social science and human-computer interaction fields, but it has also matured over the last few years in social network analysis and mining. While the main target audience corresponds to computer, network, and web data scientists, this brief might also help increase the visibility of the topic by bridging different closely related research fields. Practitioners, researchers and students interested in social networks, web search, data mining, computational social science and human-computer interaction will also find this brief useful research material .

Computational Approaches to the Network Science of Teams

Computational Approaches to the Network Science of Teams
Author: Liangyue Li,Hanghang Tong
Publsiher: Cambridge University Press
Total Pages: 164
Release: 2020-12-03
ISBN 10: 110849854X
ISBN 13: 9781108498548
Language: EN, FR, DE, ES & NL

Computational Approaches to the Network Science of Teams Book Review:

Surveys recent models and algorithms characterizing, predicting, optimizing, and explaining team performance in a variety of settings.

Protein Interaction Networks

Protein Interaction Networks
Author: Aidong Zhang
Publsiher: Cambridge University Press
Total Pages: 278
Release: 2009-04-06
ISBN 10: 0521888956
ISBN 13: 9780521888950
Language: EN, FR, DE, ES & NL

Protein Interaction Networks Book Review:

The first full survey of statistical, topological, data-mining, and ontology-based methods for analyzing protein-protein interaction networks.

Computational Network Theory

Computational Network Theory
Author: Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl
Publsiher: John Wiley & Sons
Total Pages: 280
Release: 2015-05-04
ISBN 10: 3527691545
ISBN 13: 9783527691548
Language: EN, FR, DE, ES & NL

Computational Network Theory Book Review:

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Computational Social Network Analysis

Computational Social Network Analysis
Author: Ajith Abraham,Aboul-Ella Hassanien,Vaclav Snášel
Publsiher: Springer
Total Pages: 485
Release: 2012-03-01
ISBN 10: 9781447125327
ISBN 13: 1447125320
Language: EN, FR, DE, ES & NL

Computational Social Network Analysis Book Review:

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities. This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems. Topics and Features: Provides an overview social network tools, and explores methods for discovering key players in social networks, designing self-organizing search systems, and clustering blog sites, surveys techniques for exploratory analysis and text mining of social networks, approaches to tracking online community interaction, and examines how the topological features of a system affects the flow of information, reviews the models of network evolution, covering scientific co-citation networks, nature-inspired frameworks, latent social networks in e-Learning systems, and compound communities, examines the relationship between the intent of web pages, their architecture and the communities who take part in their usage and creation, discusses team selection based on members’ social context, presents social network applications, including music recommendation and face recognition in photographs, explores the use of social networks in web services that focus on the discovery stage in the life cycle of these web services. This useful and comprehensive volume will be indispensible to senior undergraduate and postgraduate students taking courses in Social Intelligence, as well as to researchers, developers, and postgraduates interested in intelligent-social-networks research and related areas.

Statistical Analysis of Network Data with R

Statistical Analysis of Network Data with R
Author: Eric D. Kolaczyk,Gábor Csárdi
Publsiher: Springer
Total Pages: 207
Release: 2014-05-22
ISBN 10: 1493909835
ISBN 13: 9781493909834
Language: EN, FR, DE, ES & NL

Statistical Analysis of Network Data with R Book Review:

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Complex Networks

Complex Networks
Author: Kayhan Erciyes
Publsiher: CRC Press
Total Pages: 320
Release: 2014-09-06
ISBN 10: 1466571675
ISBN 13: 9781466571679
Language: EN, FR, DE, ES & NL

Complex Networks Book Review:

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 in Cognitive Psychology

Network Science in Cognitive Psychology
Author: Michael S. Vitevitch
Publsiher: Routledge
Total Pages: 204
Release: 2019-11-26
ISBN 10: 1000740684
ISBN 13: 9781000740684
Language: EN, FR, DE, ES & NL

Network Science in Cognitive Psychology Book Review:

This volume provides an integrative review of the emerging and increasing use of network science techniques in cognitive psychology, first developed in mathematics, computer science, sociology, and physics. The first resource on network science for cognitive psychologists in a growing international market, Vitevitch and a team of expert contributors provide a comprehensive and accessible overview of this cutting-edge topic. This innovative guide draws on the three traditional pillars of cognitive psychological research–experimental, computational, and neuroscientific–and incorporates the latest findings from neuroimaging. The network perspective is applied to the fundamental domains of cognitive psychology including memory, language, problem-solving, and learning, as well as creativity and human intelligence, highlighting the insights to be gained through applying network science to a wide range of approaches and topics in cognitive psychology Network Science in Cognitive Psychology will be essential reading for all upper-level cognitive psychology students, psychological researchers interested in using network science in their work, and network scientists interested in investigating questions related to cognition. It will also be useful for early career researchers and students in methodology and related courses.

Biological Network Analysis

Biological Network Analysis
Author: Pietro Hiram Guzzi,Swarup Roy
Publsiher: Elsevier
Total Pages: 210
Release: 2020-05-11
ISBN 10: 0128193514
ISBN 13: 9780128193518
Language: EN, FR, DE, ES & NL

Biological Network Analysis Book Review:

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes Includes a discussion of various graph theoretic and data analytics approaches