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.

This book addresses and examines the impacts of applications and services for data management and analysis, such as infrastructure, platforms, software, and business processes, on both academia and industry. The chapters cover effective approaches in dealing with the inherent complexity and increasing demands of big data management from an applications perspective. Various case studies included have been reported by data analysis experts who work closely with their clients in such fields as education, banking, and telecommunications. Understanding how data management has been adapted to these applications will help students, instructors and professionals in the field. Application areas also include the fields of social network analysis, bioinformatics, and the oil and gas industries.

The book addresses the issue of interdisciplinary understanding of collaboration on the topic of social network studies. Researchers and practitioners from various disciplines including sociology, computer science, socio-psychology, public health, complex systems, and management science have worked largely independently, each with quite different principles, terminologies, theories. and methodologies. The book aims to fill the gap among these disciplines with a number of the latest interdisciplinary collaboration studies.

The book includes both invited and contributed chapters dealing with advanced methods and theoretical development for the analysis of social networks and applications in numerous disciplines. Some authors explore new trends related to network measures, multilevel networks and clustering on networks, while other contributions deepen the relationship among statistical methods for data mining and social network analysis. Along with the new methodological developments, the book offers interesting applications to a wide set of fields, ranging from the organizational and economic studies, collaboration and innovation, to the less usual field of poetry. In addition, the case studies are related to local context, showing how the substantive reasoning is fundamental in social network analysis. The list of authors includes both top scholars in the field of social networks and promising young researchers. All chapters passed a double blind review process followed by the guest editors. This edited volume will appeal to students, researchers and professionals.

Reveals how consumer choice can be better understood and influenced using social networks analysis (SNA) Intuitively, we all appreciate that we can be influenced by our friends and peers in what we do, how we behave, and what products we consume. Until recently, it has been difficult to measure this interdependence, mainly because data on social networks was difficult to collect and not readily available. More and more companies such as mobile phone carriers or social networking sites such as Facebook are collecting such data electronically. Daniel Birke illustrates in compelling real-world case studies how companies use social networks for marketing purposes and which statistical analysis and unique datasets can be used. Social Networks and their Economics: Explores network effects and the analysis of social networks, whilst providing an overview of the state-of-the art research. Looks at consumption interdependences between friends and peers: Who is influencing who through which channels and to what degree? Presents statistical methods and research techniques that can be used in the analysis of social networks. Examines SNA and its practical application for marketing purposes. Features a supporting website www.wiley.com/go/social_networks featuring SNA visualizations and business case studies. Aimed at post-graduate students involved in social network analysis, industrial economics, innovation and consumer marketing, this book offers a unique perspective from both an academic and practitioner point of view on how social networks can help understand and influence consumer behaviour. This book will prove to be a useful resource for marketing practitioners from companies where social network data is available and for consulting companies who advise businesses on marketing and social media related issues.

Social network analysis is a methodological tool used to investigate social networks, or collections of individuals, groups, or organizations and their shared relationships. Sport researchers increasingly have applied social network analysis to a variety of sport contexts, from team interactions among players and coaches to tailgating among fans to mega sporting events and their organizers and other stakeholders. An opportunity now exists to expand this research and generate new insights regarding myriad sport contexts and applications. This book provides an overview of the growing body of social network analysis research in sport. Additionally, it offers practical guidance for applying social network analysis within different sport settings and for using this methodology with new research applications.

Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting social network data, this book will get you up-to-speed on the theory and skills you need to conduct social network analysis. Using simple language and equations, the authors provide expert, clear insight into every step of the research process—including basic maths principles—without making assumptions about what you know. With a particular focus on NetDraw and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way. In addition to the fundamentals of network analysis and the research process, this Second Edition focuses on: Digital data and social networks like Twitter Statistical models to use in SNA, like QAP and ERGM The structure and centrality of networks Methods for cohesive subgroups/community detection Supported by new chapter exercises, a glossary, and a fully updated companion website, this text is the perfect student-friendly introduction to social network analysis.

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 (SNA) is a technique that is used to determine knowledge flows and gaps in mapping social networks for various knowledge types. Social Networking: The Essence of Innovation discusses how social networking and SNA can influence innovation in an organization through the presentation of a broad range of concepts, examples, and case studies. The book's initial chapters deal with developing personal knowledge networks, linking social networking to innovation and strategic intelligence, and exploring synergies among knowledge management, business intelligence, and competitive intelligence. The introduction, an overview of social network analysis, is followed by case studies and an exploration of applications, including knowledge mapping and the analysis of organizations. Software systems devoted to these areas are described, citing results achieved, and a final chapter takes a look at future prospects, making this an essential tool for managers, executives, business analysts, educators, and strategic planners.