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.

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.

This collection of contributed chapters demonstrates a wide range of applications within two overlapping research domains: social media analysis and social network analysis. Various methodologies were utilized in the twelve individual chapters including static, dynamic and real-time approaches to graph, textual and multimedia data analysis. The topics apply to reputation computation, emotion detection, topic evolution, rumor propagation, evaluation of textual opinions, friend ranking, analysis of public transportation networks, diffusion in dynamic networks, analysis of contributors to communities of open source software developers, biometric template generation as well as analysis of user behavior within heterogeneous environments of cultural educational centers. Addressing these challenging applications is what makes this edited volume of interest to researchers and students focused on social media and social network analysis.

Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology. This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies. Part I provides background in the history and concepts of social media and social networks. Also included here is social network analysis, which flows from measuring, to mapping, and modeling collections of connections. The next part focuses on the detailed operation of the free and open-source NodeXL extension of Microsoft Excel, which is used in all exercises throughout this book. In the final part, each chapter presents one form of social media, such as e-mail, Twitter, Facebook, Flickr, and Youtube. In addition, there are descriptions of each system, the nature of networks when people interact, and types of analysis for identifying people, documents, groups, and events. - Walks you through NodeXL, while explaining the theory and development behind each step, providing takeaways that can apply to any SNA - Demonstrates how visual analytics research can be applied to SNA tools for the mass market - Includes case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and wikis - Download companion materials and resources at https://nodexl.codeplex.com/documentation

Social Network Analysis of Disaster Response, Recovery, and Adaptation covers systematic social network analysis and how people and institutions function in disasters, after disasters, and the ways they adapt to hazard settings. As hazards become disasters, the opportunities and constraints for maintaining a safe and secure life and livelihood become too strained for many people. Anecdotally, and through many case studies, we know that social interactions exacerbate or mitigate those strains, necessitating a concerted, intellectual effort to understand the variation in how ties within, and outside, communities respond and are affected by hazards and disasters. - Examines the role of societal relationships in a disaster context, incorporating theory and case studies by experts in the field - Integrates research in the areas of social network analysis and inter-organizational networks - Presents a range of studies from around the world, employing different approaches to network analysis in disaster contexts

In Communities and Networks, Katherine Giuffre takes the science of social network analysis and applies it to key issues of living in communities, especially in urban areas, exploring questions such as: How do communities shape our lives and identities? How do they foster either conformity or innovation? What holds communities together and what happens when they fragment or fall apart? How is community life changing in response to technological advances? Refreshingly accessible and built on fascinating case examples, this unique book provides not only the theoretical grounding necessary to understand how and why the burgeoning area of social network analysis can be useful in studying communities, but also clear technical explanations of the tools of network analysis and how to gather and analyze real-world network data. Network analysis allows us to see community life in a new perspective, with sometimes surprising results and insights, and this book enables readers to gain a deeper understanding of social life and the relationships that build (and break) communities. This engaging text will be an exciting new resource for upper-level undergraduate and beginning graduate students in a wide range of courses including social network analysis, community studies, urban studies, organizational studies, and quantitative methods.

This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.