Das Interesse an der Netzwerkanalyse nimmt rapide zu. Bisher fehlt es jedoch an empirisch orientierten Einführungen. Das interdisziplinäre Autorenteam führt daher praxisorientiert in die Grundlagen und Methoden der empirischen Analyse sozialer Netzwerke ein. Schritt für Schritt wird der Forschungsprozess von der Untersuchungsplanung über die Auswertungsmethodik bis zur Präsentation der Ergebnisse erläutert. Damit ist das Lehrbuch für den Einsatz in Lehre, Forschung und Praxis geeignet. This textbook provides an introduction to the process of empirical network research. In an action-oriented approach, it features explicated learning goals, numerous reference examples, and exercises that facilitate successful learning. Integrating their different disciplinary perspectives, the authors address an interdisciplinary audience of teachers, researchers, and practitioners alike.

This volume engages the interface between the development of human lives and social relational networks. It focuses on the integration of two subfields of sociology/social science--the life course and social networks. Research practitioners studying social networks typically focus on social structure or social organization, ignoring the complex lives of the people in those networks. At the same time, life course researchers tend to focus on individual lives without necessarily studying the contexts of social relationships in which lives are embedded and “linked” to one another through social networks. These patterns are changing and this book creates an audience of researchers who will better integrate the two subfields. It covers the role of social networks across the life span, from childhood and adolescence, to midlife, through old age.

During the last 25 years, life expectancy at age 50 in the United States has been rising, but at a slower pace than in many other high-income countries, such as Japan and Australia. This difference is particularly notable given that the United States spends more on health care than any other nation. Concerned about this divergence, the National Institute on Aging asked the National Research Council to examine evidence on its possible causes. According to Explaining Divergent Levels of Longevity in High-Income Countries, the nation's history of heavy smoking is a major reason why lifespans in the United States fall short of those in many other high-income nations. Evidence suggests that current obesity levels play a substantial part as well. The book reports that lack of universal access to health care in the U.S. also has increased mortality and reduced life expectancy, though this is a less significant factor for those over age 65 because of Medicare access. For the main causes of death at older ages-cancer and cardiovascular disease-available indicators do not suggest that the U.S. health care system is failing to prevent deaths that would be averted elsewhere. In fact, cancer detection and survival appear to be better in the U.S. than in most other high-income nations, and survival rates following a heart attack also are favorable. Explaining Divergent Levels of Longevity in High-Income Countries identifies many gaps in research. For instance, while lung cancer deaths are a reliable marker of the damage from smoking, no clear-cut marker exists for obesity, physical inactivity, social integration, or other risks considered in this book. Moreover, evaluation of these risk factors is based on observational studies, which-unlike randomized controlled trials-are subject to many biases.

The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective decision making, opinion formation, and diffusion dynamics. From a methodological perspective, the contributors to this volume devote attention to theory, field experiments, laboratory experiments, and econometrics. Theoretical work in network formation, games played on networks, repeated games, and the interaction between linking and behavior is synthesized. A number of chapters are devoted to studying social process mediated by networks. Topics here include opinion formation, diffusion of information and disease, and learning. There are also chapters devoted to financial contagion and systemic risk, motivated in part by the recent financial crises. Another section discusses communities, with applications including social trust, favor exchange, and social collateral; the importance of communities for migration patterns; and the role that networks and communities play in the labor market. A prominent role of networks, from an economic perspective, is that they mediate trade. Several chapters cover bilateral trade in networks, strategic intermediation, and the role of networks in international trade. Contributions discuss as well the role of networks for organizations. On the one hand, one chapter discusses the role of networks for the performance of organizations, while two other chapters discuss managing networks of consumers and pricing in the presence of network-based spillovers. Finally, the authors discuss the internet as a network with attention to the issue of net neutrality.

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.

Celebrated scientists Nicholas Christakis and James Fowler explain the amazing power of social networks and our profound influence on one another's lives. Your colleague's husband's sister can make you fat, even if you don't know her. A happy neighbor has more impact on your happiness than a happy spouse. These startling revelations of how much we truly influence one another are revealed in the studies of Dr. Christakis and Fowler, which have repeatedly made front-page news nationwide. In Connected, the authors explain why emotions are contagious, how health behaviors spread, why the rich get richer, even how we find and choose our partners. Intriguing and entertaining, Connected overturns the notion of the individual and provides a revolutionary paradigm-that social networks influence our ideas, emotions, health, relationships, behavior, politics, and much more. It will change the way we think about every aspect of our lives.

The present volume provides a comprehensive resource for practitioners and researchers alike-both those new to the field as well as those who already have some experience. The work covers Social Network Analysis theory and methods with a focus on current applications and case studies applied in various domains such as mobile networks, security, machine learning and health. With the increasing popularity of Web 2.0, social media has become a widely used communication platform. Parallel to this development, Social Network Analysis gained in importance as a research field, while opening up many opportunities in different application domains. Forming a bridge between theory and applications makes this work appealing to both academics and practitioners as well as graduate students.

Written by a stellar team of experts, Analyzing Social Networks is a practical book on how to collect, visualize, analyze and interpret social network data with a particular emphasis on the use of the software tools UCINET and Netdraw. The book includes a clear and detailed introduction to the fundamental concepts of network analyses, including centrality, subgroups, equivalence and network structure, as well as cross-cutting chapters that helpfully show how to apply network concepts to different kinds of networks. Written using simple language and notation with few equations, this book masterfully covers the research process, including: · The initial design stage · Data collection and manipulation · Measuring key variables · Exploration of structure · Hypothesis testing · Interpretation This is an essential resource for students, researchers and practitioners across the social sciences who want to use network analysis as part of their research. 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.

This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.

While some social scientists may argue that we have always been networked, the increased visibility of networks today across economic, political, and social domains can hardly be disputed. Social networks fundamentally shape our lives and social network analysis has become a vibrant, interdisciplinary field of research. In The Oxford Handbook of Social Networks, Ryan Light and James Moody have gathered forty leading scholars in sociology, archaeology, economics, statistics, and information science, among others, to provide an overview of the theory, methods, and contributions in the field of social networks. Each of the thirty-three chapters in this Handbook moves through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. They cover both a succinct background to, and future directions for, distinctive approaches to analyzing social networks. The first section of the volume consists of theoretical and methodological approaches to social networks, such as visualization and network analysis, statistical approaches to networks, and network dynamics. Chapters in the second section outline how network perspectives have contributed substantively across numerous fields, including public health, political analysis, and organizational studies. Despite the rapid spread of interest in social network analysis, few volumes capture the state-of-the-art theory, methods, and substantive contributions featured in this volume. This Handbook therefore offers a valuable resource for graduate students and faculty new to networks looking to learn new approaches, scholars interested in an overview of the field, and network analysts looking to expand their skills or substantive areas of research.