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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

We live in a world that is paradoxically small and vast: each of us is embedded in local communities and yet we are only a few ′links′ away from anyone else in the world. This engaging book represents these interdependencies′ positive and negative consequences, their multiple effects and the ways in which a local occurrence in one part of the world can directly affect the rest. Then it demonstrates precisely how these interactions and relationships form. This is a book for the social network novice on learning how to study, think about and analyse social networks; the intermediate user, not yet familiar with some of the newer developments in the field; and the teacher looking for a range of exercises, as well as an up-to-date historical account of the field. It is divided into three sections: 1. Historical & Background Concepts 2. Levels of Analysis 3. Advances, Extensions and Conclusions The book provides a full overview of the field - historical origins, common theoretical perspectives and frameworks; traditional and current analytical procedures and fundamental mathematical equations needed to get a foothold in the field.

A comprehensive introduction to social network analysis that hones in on basic centrality measures, social links, subgroup analysis, data sources, and more Written by military, industry, and business professionals, this book introduces readers to social network analysis, the new and emerging topic that has recently become of significant use for industry, management, law enforcement, and military practitioners for identifying both vulnerabilities and opportunities in collaborative networked organizations. Focusing on models and methods for the analysis of organizational risk, Social Network Analysis with Applications provides easily accessible, yet comprehensive coverage of network basics, centrality measures, social link theory, subgroup analysis, relational algebra, data sources, and more. Examples of mathematical calculations and formulas for social network measures are also included. Along with practice problems and exercises, this easily accessible book covers: The basic concepts of networks, nodes, links, adjacency matrices, and graphs Mathematical calculations and exercises for centrality, the basic measures of degree, betweenness, closeness, and eigenvector centralities Graph-level measures, with a special focus on both the visual and numerical analysis of networks Matrix algebra, outlining basic concepts such as matrix addition, subtraction, multiplication, and transpose and inverse calculations in linear algebra that are useful for developing networks from relational data Meta-networks and relational algebra, social links, diffusion through networks, subgroup analysis, and more An excellent resource for practitioners in industry, management, law enforcement, and military intelligence who wish to learn and apply social network analysis to their respective fields, Social Network Analysis with Applications is also an ideal text for upper-level undergraduate and graduate level courses and workshops on the subject.

David Finchers THE SOCIAL NETWORK is the stunning tale of a new breed of cultural insurgent: A punk genius who sparked a revolution and changed the face of human interaction for a generation, and perhaps forever. Shot through with emotional brutality and unexpected humor, this superbly crafted film chronicles the formation of Facebook and the battles over ownership that followed upon the websites unfathomable success. With a complex, incisive screenplay by Aaron Sorkin and a brilliant cast inclu

The ego-net approach to social network analysis, which takes discrete individual actors and their contacts as its starting point, is one of the most widely used approaches in the field. This is the first textbook to take readers through each stage of ego-net research, from conception, through research design and data gathering to analysis. It starts with the basics, assuming no prior knowledge of social network analysis, but then moves on to introduce cutting edge innovations, covering both new statistical approaches to ego-net analysis and also the most recent thinking on mixing methods (quantitative and qualitative) to achieve depth and rigour. It is an absolute must for anybody wishing to explore the importance of networks.

The revised and updated edition of this bestselling text provides an accessible introduction to the theory and practice of network analysis in the social sciences. It gives a clear and authoritative guide to the general framework of network analysis, explaining the basic concepts, technical measures and reviewing the available computer programs. The book outlines both the theoretical basis of network analysis and the key techniques for using it as a research tool. Building upon definitions of points, lines and paths, John Scott demonstrates their use in clarifying such measures as density, fragmentation and centralization. He identifies the various cliques, components and circles into which networks are formed, and outlines

Social Network Analysis: Methods and Examples by Song Yang, Franziska B. Keller, and Lu Zheng prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.

A comprehensive introduction to social network analysis that hones in on basic centrality measures, social links, subgroup analysis, data sources, and more Written by military, industry, and business professionals, this book introduces readers to social network analysis, the new and emerging topic that has recently become of significant use for industry, management, law enforcement, and military practitioners for identifying both vulnerabilities and opportunities in collaborative networked organizations. Focusing on models and methods for the analysis of organizational risk, Social Network Analysis with Applications provides easily accessible, yet comprehensive coverage of network basics, centrality measures, social link theory, subgroup analysis, relational algebra, data sources, and more. Examples of mathematical calculations and formulas for social network measures are also included. Along with practice problems and exercises, this easily accessible book covers: The basic concepts of networks, nodes, links, adjacency matrices, and graphs Mathematical calculations and exercises for centrality, the basic measures of degree, betweenness, closeness, and eigenvector centralities Graph-level measures, with a special focus on both the visual and numerical analysis of networks Matrix algebra, outlining basic concepts such as matrix addition, subtraction, multiplication, and transpose and inverse calculations in linear algebra that are useful for developing networks from relational data Meta-networks and relational algebra, social links, diffusion through networks, subgroup analysis, and more An excellent resource for practitioners in industry, management, law enforcement, and military intelligence who wish to learn and apply social network analysis to their respective fields, Social Network Analysis with Applications is also an ideal text for upper-level undergraduate and graduate level courses and workshops on the subject.

Incorporating the most important and cutting-edge developments in the field, this bestselling text introduces newcomers to the key theories and techniques of social network analysis and guides more experienced analysts in their own research. New to This Edition: A chapter on data collection, covering a crucial phase of the research process Fully updated examples reiterate the continued importance of social network analysis in an increasingly interconnected world Detailed ‘Further Reading’ sections help you explore the wider literature Practical exercises including real-world examples of social networks enable you to apply your learning Expanded and brought right up-to-date, this classic text remains the indispensable guide to social network analysis for students, lecturers and researchers throughout the social sciences.

Using social media and peer-to-peer networks to teach people about science and health may seem like an obvious strategy. Yet recent research suggests that systematic reliance on social networks may be a recipe for inequity. People are not consistently inclined to share information with others around them, and many people are constrained by factors outside of their immediate control. Ironically, the highly social nature of humankind complicates the extent to which we can live in a society united solely by electronic media.Stretching well beyond social media, this book documents disparate tendencies in the ways people learn and share information about health and science. By reviewing a wide array of existing research―ranging from a survey of New Orleans residents in the weeks after Hurricane Katrina to analysis of Twitter posts related to H1N1 to a physician-led communication campaign explaining the benefits of vaginal birth―Brian G. Southwell explains why some types of information are more likely to be shared than others and how some people never get exposed to seemingly widely available information.This book will appeal to social science students and citizens interested in the role of social networks in information diffusion and yet it also serves as a cautionary tale for communication practitioners and policymakers interested in leveraging social ties as an inexpensive method to spread information.

Incorporating the most important and cutting-edge developments in the field, this bestselling text introduces newcomers to the key theories and techniques of social network analysis and guides more experienced analysts in their own research. New to This Edition: A chapter on data collection, covering a crucial phase of the research process Fully updated examples reiterate the continued importance of social network analysis in an increasingly interconnected world Detailed ‘Further Reading’ sections help you explore the wider literature Practical exercises including real-world examples of social networks enable you to apply your learning Expanded and brought right up-to-date, this classic text remains the indispensable guide to social network analysis for students, lecturers and researchers throughout the social sciences.

David Knoke and Song Yang′s Social Network Analysis, Third Edition provides a concise introduction to the concepts and tools of social network analysis. The authors convey key material while at the same time minimizing technical complexities. The examples are simple: sets of 5 or 6 entities such as individuals, positions in a hierarchy, political offices, and nation-states, and the relations between them include friendship, communication, supervision, donations, and trade. The new edition reflects developments and changes in practice over the past decade. The authors also describe important recent developments in network analysis, especially in the fifth chapter. Exponential random graph models (ERGMs) are a prime example: when the second edition was published, P* models were the recommended approach for this, but they have been replaced by ERGMs. Finally, throughout the volume, the authors comment on the challenges and opportunities offered by internet and social media data.

Does your startup rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available.Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you'll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You'll also learn how to use Python and other open source tools—such as NetworkX, NumPy, and Matplotlib—to gather, analyze, and visualize social data. This book is the perfect marriage between social network theory and practice, and a valuable source of insight and ideas.Discover how internal social networks affect a company’s ability to performFollow terrorists and revolutionaries through the 1998 Khobar Towers bombing, the 9/11 attacks, and the Egyptian uprisingLearn how a single special-interest group can control the outcome of a national electionExamine relationships between companies through investment networks and shared boards of directorsDelve into the anatomy of cultural fads and trends—offline phenomena often mediated by Twitter and Facebook

In the thoroughly updated edition of Social Network Analysis, authors David Knoke and Song Yang take into account the vast number of changes in the field that have occurred in the 25 years since the first edition was published. They cover various issues in basic network concepts, data collection, and network analytical methodology. They succinctly illustrate the concepts and methods related to substantive social network research problems, citing examples ranging from children′s play groups to organizations, communities, and international systems. Readers can also analyze the real and artificial datasets incorporated throughout the book.Key Features of the Second Edition· Expands on the previous edition by incorporating new basic social network developments· Provides a general overview of fundamental theoretical and methodological topics· Clearly presents concepts by using concise language and avoiding technical complexitiesIntended AudienceThe text′s informal approach will appeal to both students and professionals in the humanities and social sciences.Learn more about "The Little Green Book" - QASS Series! Click Here

Social Network Theory and Educational Change offers a provocative and fascinating exploration of how social networks in schools can impede or facilitate the work of education reform. Drawing on the work of leading scholars, the book comprises a series of studies examining networks among teachers and school leaders, contrasting formal and informal organizational structures, and exploring the mechanisms by which ideas, information, and influence flow from person to person and group to group. The case studies provided in the book reflect a rich variety of approaches and methodologies, showcasing the range and power of this dynamic new mode of analysis. An introductory chapter places social network theory in context and explains the basic tools and concepts, while a concluding chapter points toward new directions in the field. Taken together, they make a powerful statement: that the success or failure of education reform ultimately is not solely the result of technical plans and blueprints, but of the relational ties that support or constrain the pace, depth, and direction of change. This unique volume provides an invaluable introduction to an emerging and increasingly important field of education research.Contributors Include: Stephen P. Borgatti and Brandon Ofem; Cynthia E. Coburn, Linda Choi, and Willow Mata; Allison Atteberry and Anthony S. Bryk; Russell P. Cole and Elliot H. Weinbaum; Nienke M. Moolenaar and Peter Sleegers; Kira J. Baker-Doyle and Susan A. Yoon; James P. Spillane, Kaleen Healey, and Chong Min Kim; William R. Penuel, Kenneth A. Frank, and Ann Krause; Kara Finnigan and Alan J. Daly; Julie M. Hite, Steven J. Hite, Christopher B. Mugimu, and Yusuf Nsubuga; Kenneth A. Frank, Chong Min Kim, and Dale Belman; Jorge Ávila de Lima.

The ego-net approach to social network analysis, which takes discrete individual actors and their contacts as its starting point, is one of the most widely used approaches in the field. This is the first textbook to take readers through each stage of ego-net research, from conception, through research design and data gathering to analysis. It starts with the basics, assuming no prior knowledge of social network analysis, but then moves on to introduce cutting edge innovations, covering both new statistical approaches to ego-net analysis and also the most recent thinking on mixing methods (quantitative and qualitative) to achieve depth and rigour. It is an absolute must for anybody wishing to explore the importance of networks.

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Social network analysis, which focuses on relationships among social entities, is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.

David Knoke and Song Yang′s Social Network Analysis, Third Edition provides a concise introduction to the concepts and tools of social network analysis. The authors convey key material while at the same time minimizing technical complexities. The examples are simple: sets of 5 or 6 entities such as individuals, positions in a hierarchy, political offices, and nation-states, and the relations between them include friendship, communication, supervision, donations, and trade. The new edition reflects developments and changes in practice over the past decade. The authors also describe important recent developments in network analysis, especially in the fifth chapter. Exponential random graph models (ERGMs) are a prime example: when the second edition was published, P* models were the recommended approach for this, but they have been replaced by ERGMs. Finally, throughout the volume, the authors comment on the challenges and opportunities offered by internet and social media data.

Social Network Analysis: Methods and Examples by Song Yang, Franziska B. Keller, and Lu Zheng prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.