In the last years, adaptive networks have been discovered simultaneously in different fields as a universal framework for the study of self-organization phenomena. Understanding the mechanisms behind these phenomena is hoped to bring forward not only empirical disciplines such as biology, sociology, ecology, and economy, but also engineering disciplines seeking to employ controlled emergence in future technologies. This volume presents new analytical approaches, which combine tools from dynamical systems theory and statistical physics with tools from graph theory to address the principles behind adaptive self-organization. It is the first class of approaches that is applicable to continuous networks. The volume discusses the mechanisms behind three emergent phenomena that are prominently discussed in the context of biological and social sciences:• synchronization,• spontaneous diversification, and• self-organized criticality.Self-organization in continuous adaptive networks contains extended research papers. It can serve as both, a review of recent results on adaptive self-organization as well as a tutorial of new analytical methodsSelf-organization in continuous adaptive networks is ideal for academic staff and master/research students in complexity and network sciences, in engineering, physics and maths.

In the last years, adaptive networks have been discovered simultaneously in different fields as a universal framework for the study of self-organization phenomena. Understanding the mechanisms behind these phenomena is hoped to bring forward not only empirical disciplines such as biology, sociology, ecology, and economy, but also engineering disciplines seeking to employ controlled emergence in future technologies. This volume presents new analytical approaches, which combine tools from dynamical systems theory and statistical physics with tools from graph theory to address the principles behind adaptive self-organization. It is the first class of approaches that is applicable to continuous networks. The volume discusses the mechanisms behind three emergent phenomena that are prominently discussed in the context of biological and social sciences: – synchronization, – spontaneous diversification, and – self-organized criticality. Self-organization in continuous adaptive networks contains extended research papers. It can serve as both, a review of recent results on adaptive self-organization as well as a tutorial of new analytical methods. Self-organization in continuous adaptive networks is ideal for academic staff and master/research students in complexity and network sciences, in engineering, physics and maths.

This practical guide shows how to facilitate collaboration among diverse individuals and organizations to navigate complexity and create change in our interconnected world. The social and environmental challenges we face today are not only complex, they are also systemic and structural and have no obvious solutions. They require diverse combinations of people, organizations, and sectors to coordinate actions and work together even when the way forward is unclear. Even so, collaborative efforts often fail because they attempt to navigate complexity with traditional strategic plans, created by hierarchies that ignore the way people naturally connect. By embracing a living-systems approach to organizing, impact networks bring people together to build relationships across boundaries; leverage the existing work, skills, and motivations of the group; and make progress amid unpredictable and ever-changing conditions. As a powerful and flexible organizing system that can span regions, organizations, and silos of all kinds, impact networks underlie some of the most impressive and large-scale efforts to create change across the globe. David Ehrlichman draws on his experience as a network builder; interviews with dozens of network leaders; and insights from the fields of network science, community building, and systems thinking to provide a clear process for creating and developing impact networks. Given the increasing complexity of our society and the issues we face, our ability to form, grow, and work through networks has never been more essential.

Adding one and one makes two, usually. But sometimes things add up to more than the sum of their parts. This observation, now frequently expressed in the maxim “more is different”, is one of the characteristic features of complex systems and, in particular, complex networks. Along with their ubiquity in real world systems, the ability of networks to exhibit emergent dynamics, once they reach a certain size, has rendered them highly attractive targets for research. The resulting network hype has made the word “network” one of the most in uential buzzwords seen in almost every corner of science, from physics and biology to economy and social sciences. The theme of “more is different” appears in a different way in the present v- ume, from the viewpoint of what we call “adaptive networks.” Adaptive networks uniquely combine dynamics on a network with dynamical adaptive changes of the underlying network topology, and thus they link classes of mechanisms that were previously studied in isolation. Here adding one and one certainly does not make two, but gives rise to a number of new phenomena, including highly robust se- organization of topology and dynamics and other remarkably rich dynamical beh- iors.

This book constitutes the refereed proceedings of the 6th IFIP TC 6 International Workshop on Self-Organizing Systems, IWSOS 2012, held in Delft, The Netherlands, in March 2012. The 5 revised full papers and 5 short papers presented together with 2 invited papers were carefully selected from 25 full paper and 8 short paper submissions. The papers address the following key topics: design and analysis of self-organizing and self-managing systems; inspiring models of self-organization in nature and society; structure, characteristics and dynamics of self-organizing networks; techniques and tools for modeling self-organizing systems; robustness and adaptation in self-organizing systems; self-organization in complex networks like peer-to-peer, sensor, ad-hoc, vehicular and social networks; control of self-organizing systems; decentralized power management in the smart grid; self-organizing group and pattern formation; self-organizing mechanisms for task allocation, coordination and resource allocation; self-organizing information dissemination and content search; and risks and limits of self-organization.

Graph theory can be applied to ecological questions in many ways, and more insights can be gained by expanding the range of graph theoretical concepts applied to a specific system. But how do you know which methods might be used? And what do you do with the graph once it has been obtained? This book provides a broad introduction to the application of graph theory in different ecological systems, providing practical guidance for researchers in ecology and related fields. Readers are guided through the creation of an appropriate graph for the system being studied, including the application of spatial, spatio-temporal, and more abstract structural process graphs. Simple figures accompany the explanations to add clarity, and a broad range of ecological phenomena from many ecological systems are covered. This is the ideal book for graduate students and researchers looking to apply graph theoretical methods in their work.

Adopting a cross-disciplinary approach, the review character of this monograph sets it apart from specialized journals. The editor is advised by a first-class board of international scientists, such that the carefully selected and invited contributions represent the latest and most relevant findings.