Managing Complex Networks addresses the key issue in governance: how do we manage networks? This book shows networks as a fundamental characteristic of modern societies. It develops a network management approach to governance and public management, showing how governments can manage policy networks to enhance policy performance and public service delivery.
Although the concept of policy networks is now well-established in the field, most research has to content itself with description and analysis of their contribution to policy failure. This book goes further. It accepts policy networks as a fundamental characteristic of modern societies and presents an overview of the strategies for the management of these networks, as well as illustrating the various strategies for intervention.
Concerned with the management of complex long-term engineering projects, this important volume, of great interest to postgraduate students of business, technology management and engineering, reports on a set of rich, novel and unique findings concerning the conduct and management of three high profile and complex projects. The major investments which constitute complex long-term projects represent an increasingly important source of economic activity, often with particularly significant consequences for economic growth and public policy. This informative volume expertly contributes to broader debates concerning new organizational forms, knowledge management and organizational learning and the management of innovation in project-based settings.
Advances in public management sciences have long indicated the empirical finding that the normal state of public management systems is complex and that its dynamics are non-linear. Complex systems are subject to system pressures, system shocks, chance events, path-dependency and self-organisation. Arguing that complexity is an ever-present characteristic of our developed societies and governance systems that should be accepted, understood and adopted into management strategies, the original essays collected in this book aim to increase our understanding of complex governance processes and to propose new strategies for how public managers can deal with complexity in order to achieve high-quality research. The authors collected here use theoretical frameworks grounded in empirical research to analyze and explain how non-linear dynamics, self-organisation of many agents and the co-evolution of processes combine to generate the evolution of governance processes, especially for public urban and metropolitan investments. Managing Complex Governance Systems: Dynamics, Self-Organization and Coevolution in Public Investments offers readers an increased understanding of the main objective of public management in complexity--namely complex process system--and a strategy for accepting and dealing with complexity based on the idea of dual thinking and dual action strategies satisfying the desires of controlling processes and the need to adjust to changes simultaneously.
This Element presents one of the most recent developments in network science in a highly accessible style. This Element will be of interest to interdisciplinary scientists working in network science, in addition to mathematicians working in discrete topology and geometry and physicists working in quantum gravity.
Complex Social Networks is a newly emerging (hot) topic with applications in a variety of domains, such as communication networks, engineering networks, social networks, and biological networks. In the last decade, there has been an explosive growth of research on complex real-world networks, a theme that is becoming pervasive in many disciplines, ranging from mathematics and computer science to the social and biological sciences. Optimization of complex communication networks requires a deep understanding of the interplay between the dynamics of the physical network and the information dynamics within the network. Although there are a few books addressing social networks or complex networks, none of them has specially focused on the optimization perspective of studying these networks. This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology.
Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelligence, computational and systems biology, cognitive science, computational linguistics, and mathematical chemistry. It may also be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.
