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 year’s volume of Advances in Social Network Analysis contains the p- ceedings for the Second International Workshop on Social Network Analysis (SNAKDD 2008). The annual workshop co-locates with the ACM SIGKDD - ternational Conference on Knowledge Discovery and Data Mining (KDD). The second SNAKDD workshop was held with KDD 2008 and received more than 32 submissions on social network mining and analysis topics. We accepted 11 regular papers and 8 short papers. Seven of the papers are included in this volume. In recent years, social network research has advanced signi?cantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale o?ine social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are - creasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topol- ical properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining socialnetworks.These issues haveimportant implications oncom- nitydiscovery,anomalydetection,trendpredictionandcanenhanceapplications in multiple domains such as information retrieval, recommendation systems, - curity and so on.

This book constitutes the refereed proceedings of the Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, held in Bethseda, MD, USA, in March 2010. The 26 revised full papers and 23 revised poster papers presented together with 4 invited and keynote papers were carefully reviewed and selected from 78 initial submissions. The papers cover a wide range of interesting topics such as social network analysis, modeling, machine learning and data mining, social behaviors, public health, cultural aspects, effects and search.

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.

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.

This book provides an introduction to the theories, methods, and applications that constitute the social network perspective. Unlike more general texts, this title is designed for those current and aspiring educational researchers learning how to study, conceptualize, and analyze social networks. The author′s main intent is to encourage you to consider the social network perspective in light of your emerging research interests and evaluate how well this perspective illuminates the social complexities surrounding educational phenomena. Whether your interests lie in examining a peer′s influence on students′ achievement, the relationship between social support and teacher retention, or how the pattern of relations among parents contributes to schools′ norms, the tools introduced in this book will provide you with a slightly different take on these and other phenomena. Unlike other approaches, this perspective accounts for the importance of relationships within formal structures, and the informal patterns of interaction that emerge, sustain, or recede. Relying on diverse examples drawn from the educational research literature, this book makes explicit how the theories and methods associated with social network analysis can be used to better describe and explain the social complexities surrounding varied educational phenomena.

This book introduces the non-specialist reader to the principal ideas, nature and purpose of social network analysis. Social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals achieve their goals. Social network theory maps these relationships between individual actors and has become hugely influential across the social sciences. Assuming no prior knowledge of quantitative sociology, and avoiding technicalities, this book presents the key ideas in context through examples and illustrations. Using a structured approach to understanding work in this area, John Scott signposts further reading and online sources so that readers can develop their knowledge and skills to become practitioners of this research method. A series of Frequently Asked Questions takes the reader through the main objections raised against social network analysis. The new edition updates the overview of the discipline with more recent work and current research in sociology.

The world today is becoming a highly connected place. Firms, consumers and the devices they use are increasingly part of a complex, global network of connected entities. These networks represent a gold mine for marketing scholars that may be interested in developing a better understanding of consumer behavior, and for practitioners who are keen to discover new ways of gaining and retaining customers. This cumulative dissertation focuses on the use of network analysis to generate and analyze novel data in marketing. The work moves beyond the scope of traditional social network analysis to consider networks composed of different types of nodes at varying levels of granularity. The key aim is to demonstrate that network analysis can be used to investigate novel explanatory and outcome variables that hold contextual meaning and can deepen our understanding of the research question at hand. Essay 1 develops a predictive method based on product networks to identify customer projects in a retail setting. Essays 2 and 3 show the value of network analysis in new product development by recasting product ideas as networks of their constituent features. Essay 4 leverages network thinking to enable novel analyses of the fragmented and anonymized event data that will be generated in abundance by the Internet of Things. Ultimately, the essays showcase a selection of advanced applications of network analysis in marketing science that are arguably of value to scholars as well as practitioners, and likely to gain in relevance in the future.

Models and Methods in Social Network Analysis, first published in 2005, 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.