In line with the multi-disciplinary nature of network research, this edited volume collects both empirical and conceptual contributions that nurture the debate on network research, specifically dealing with the topics of network performance and agency. The contributions draw on different literatures and epistemic approaches and address different levels of analysis, both from a static and a dynamic point of view. It will be of great interest to academics and students developing research in the field of network studies. It will also be of interest to scholars of operations management, organization studies, strategy, innovation, financial management and business history.
Perspectives on Social Network Research covers the proceedings of the Mathematical Social Science Board's Advanced Research Symposium on Social Networks held at Dartmouth College, Hanover, New Hampshire, on September 18-21, 1975. This symposium was organized to survey research on social networks as well as review and criticize major research thrusts involving network studies of social behavior. The book covers topics such as the Davis/Holland/Leinhardt studies, structural sociometry, network analysis of the diffusion of innovations, and the deterministic models of social networks. Also covered are topics such as structural control models for group processes, social clusters and opinion clusters, equilibrating processes in social networks, and estimation of population totals by use of snowball samples. The text is recommended for sociologists, anthropologists, and psychologists, especially those who would like to know more about social network and are currently engaged in research in that particular field.
Networks have been described in terms of metaphors, governance arrangements and structural or institutional arrangements. These different perspectives of networks come out of a variety of disciplines, including political science, public administration, urban affairs, social welfare, public management and organizational/sociological research. This wealth of research, while contributing to a deeper understanding of networks, presents a dilemma which is addressed by this book. That is the question of whether there is a theory of public networks that informs networks in their various forms, and is there a need for a new theory of networks? More importantly, is network research still relevant to practice? Does network theory improve the process of governance? Are different terms and/or approaches actually the same or different? What do these different approaches mean to theory? This book deeply explores and integrates existing network theory and related theories from a number of perspectives, levels and jurisdictions to develop a framework to guide network design, governance and management. The book focuses on the important issue of network performance, looking at networks as bounded and consciously arranged; the actors who participate in them design the relationships among a bounded set of individual organizations to purse common objectives. Finally, the chapters tease out the variety of governance modes or regimes that intersect with network governance. This book offers a comprehensive, integrative, interdisciplinary approach that enables specialists, practitioners and administrators across a wide array of interests and fields to formulate and work on problems using a common language, analytical framework and theoretical basis.
This edited collection provides a state-of-the art overview of research on willingness to communicate (WTC) in a second and foreign language. In particular, it includes innovative studies seeking to demonstrate the ways in which WTC can be examined within the framework of complex dynamic systems, how the construct is related to self-assessment, reticence and extroversion, and what is signifies in the case of immigrants. Another group of papers is related to the role of technology in fostering WTC in different contexts. The volume also comprises papers that touch on methodological issues in the study of WTC such as experience case sampling, the network approach or the integration of the macro- and micro-perspective. The book will be of values to researchers interested in the study of WTC but will also provide inspiration for students, teachers and materials writers.
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.
‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
In recent decades, the international economy has witnessed fundamental changes in the way manufacturing is organised: products are no longer manufactured in their entirety in a single location. Instead, the production process is often split across a number of stages located in countries that are frequently far apart from each other. By spreading out their manufacturing and supply chain activities globally through international investment and intra-firm trade, Multinational enterprises (MNEs) play a focal role in this reorganisation of production. Our ability to understand the global economy, therefore, requires an understanding of the interdependencies between the entities involved in such fragmented production. Traditional methods and statistical approaches are insufficient to address this challenge. Instead, an approach is required that allows us to account for these interdependencies. The most promising approach so far is network analysis. ‘Networks of International Trade and Investment’ makes a case for the use of network analysis alongside existing techniques in order to investigate pressing issues in international business and economics. The authors put forward a range of well-informed studies that examine compelling topics such as the role of emerging economies in global trade and the evolution of world trade patterns. They look at how network analysis, as both an approach and a methodology, can explain international business and economics phenomena, in particular, in relation to international trade and investment. Providing a comprehensive but accessible explanation of the applications of network analysis and some of the most recent methodological advances in its field, this edited volume is an important contribution to research in international trade and investment.
This book presents a perspective of network analysis as a tool to find and quantify significant structures in the interaction patterns between different types of entities. Moreover, network analysis provides the basic means to relate these structures to properties of the entities. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether, these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different global network measures, types of random graph models, centrality indices, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy – the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing a glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Different aspects of network analysis literacy – understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation – are deepened in various exercises with provided solutions. This text is an excellent, if not the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
