Social network analysis, a method for analyzing relationships between social entities, has expanded over the last decade as new research has been done in this area. How can these new developments be applied effectively in the behavioral and social sciences disciplines? In Advances in Social Network Analysis, a team of leading methodologists in network analysis addresses this issue. They explore such topics as ways to specify the network contents to be studied, how to select the method for representing network structures, how social network analysis has been used to study interorganizational relations via the resource dependence model, how to use a contact matrix for studying the spread of disease in epidemiology, and how cohesion and structural equivalence network theories relate to studying social influence. It also offers statistical models for social support networks. Advances in Social Network Analysis is useful for researchers involved in general research methods and qualitative methods, and who are interested in psychology and sociology.

This work constitutes the proceedings of the Second International Workshop on Advances in Social Network and Analysis, held in Las Vegas, NV, USA in August 2008.

Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.

The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.

We live in a world that is paradoxically both 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 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 clear 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. Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.

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.

This sparkling Handbook offers an unrivalled resource for those engaged in the cutting edge field of social network analysis. Systematically, it introduces readers to the key concepts, substantive topics, central methods and prime debates. Among the specific areas covered are: Network theory Interdisciplinary applications Online networks Corporate networks Lobbying networks Deviant networks Measuring devices Key Methodologies Software applications. The result is a peerless resource for teachers and students which offers a critical survey of the origins, basic issues and major debates. The Handbook provides a one-stop guide that will be used by readers for decades to come.

Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of the importance of the information and insights that can be culled from the wealth of data inherent in the various aspects of the network, this book provides insights on measuring the relationships and flows between people, groups, organizations, computers, URLs, and more. Examines a variety of data analytic techniques that can be applied to social networks Discusses various methods of visualizing, modeling and tracking network patterns, organization, growth and change Covers the most recent research on social network analysis and includes applications to a number of domains

Social network analysis, a method for analyzing relationships between social entities, has expanded over the last decade as new research has been done in this area. How can these new developments be applied effectively in the behavioral and social sciences disciplines? In Advances in Social Network Analysis, a team of leading methodologists in network analysis addresses this issue. They explore such topics as ways to specify the network contents to be studied, how to select the method for representing network structures, how social network analysis has been used to study interorganizational relations via the resource dependence model, how to use a contact matrix for studying the spread of disease in epidemiology, and how cohesion and structural equivalence network theories relate to studying social influence. It also offers statistical models for social support networks. Advances in Social Network Analysis is useful for researchers involved in general research methods and qualitative methods, and who are interested in psychology and sociology.

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.