This volume constitutes the proceedings of NetSci-X 2020: the Sixth International School and Conference on Network Science, which was held in Tokyo, Japan, in January 2020. NetSci-X is the Network Science Society's winter conference series that covers a wide variety of interdisciplinary topics on networks. Participants come from various fields, including (but not limited to): mathematics, physics, computer science, social sciences, management and marketing sciences, organization science, communication science, systems science, biology, ecology, neuroscience, medicine, as well as business. This volume consists of contributed papers that have been accepted to NetSc-X 2020 through a rigorous peer review process. Researchers, students, and professionals will gain first-hand information about today's cutting-edge research frontier of network science.

Around the globe, there is an increasingly urgent need to provide opportunities for learners to embrace complexity; to develop the many skills and habits of mind that are relevant to today's complex and interconnected world; and to make learning more connected to our rapidly changing workplace and society. This presents an opportunity to (1) leverage new paradigms for understanding the structure and function of teaching and learning communities, and (2) to promote new approaches to developing methods, curricular materials, and resources. Network science - the study of connectivity - can play an important role in these activities, both as an important subject in teaching and learning and as a way to develop interconnected curricula. Since 2010, an international community of network science researchers and educators has come together to raise the global level of network literacy by applying ideas from network science to teaching and learning. Network Science in Education - which refers to both this community and to its activities - has evolved in response to the escalating activity in the field of network science and the need for people to be able to access the field through education channels. Network Science In Education: Transformational Approaches in Teaching and Learning appeals to both instructors and professionals, while offering case studies from a wide variety of activities that have been developed around the globe: the creation of entirely new courses and degree programs; tools for K-20 learners, teachers, and the general public; and in-depth analysis of selected programs. As network-based pedagogy and the community of practice continues to grow, we hope that the book's readers will join this vibrant network education community to build on these nascent ideas and help deepen the understanding of networks for all learners.

Generally, spontaneous pattern formation phenomena are random and repetitive, whereas elaborate devices are the deterministic product of human design.
Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both self-organized and architectural.
This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of self-architecturing systems. Particular emphasis is placed on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies.
Altogether, the aim of this work is to provide a framework for and examples of a larger class of self-architecturing systems, while addressing fundamental questions such as
> How do biological organisms carry out morphogenetic tasks so reliably?
> Can we extrapolate their self-formation capabilities to engineered systems?
> Can physical systems be endowed with information (or informational systems be embedded in physics) so as to create autonomous morphologies and functions?
> What are the core principles and best practices for the design and engineering of such morphogenetic systems?
The intended audience consists of researchers and graduate students who are working on, starting to work on, or interested in programmable self-organizing systems in a wide range of scientific fields, including computer science, robotics, bioengineering, control engineering, physics, theoretical biology, mathematics, and many others.

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 volume constitutes the proceedings of NetSci-X 2020: the Sixth International School and Conference on Network Science, which was held in Tokyo, Japan, in January 2020. NetSci-X is the Network Science Society's winter conference series that covers a wide variety of interdisciplinary topics on networks. Participants come from various fields, including (but not limited to): mathematics, physics, computer science, social sciences, management and marketing sciences, organization science, communication science, systems science, biology, ecology, neuroscience, medicine, as well as business. This volume consists of contributed papers that have been accepted to NetSc-X 2020 through a rigorous peer review process. Researchers, students, and professionals will gain first-hand information about today's cutting-edge research frontier of network science.

Around the globe, there is an increasingly urgent need to provide opportunities for learners to embrace complexity; to develop the many skills and habits of mind that are relevant to today's complex and interconnected world; and to make learning more connected to our rapidly changing workplace and society. This presents an opportunity to (1) leverage new paradigms for understanding the structure and function of teaching and learning communities, and (2) to promote new approaches to developing methods, curricular materials, and resources. Network science - the study of connectivity - can play an important role in these activities, both as an important subject in teaching and learning and as a way to develop interconnected curricula. Since 2010, an international community of network science researchers and educators has come together to raise the global level of network literacy by applying ideas from network science to teaching and learning. Network Science in Education - which refers to both this community and to its activities - has evolved in response to the escalating activity in the field of network science and the need for people to be able to access the field through education channels. Network Science In Education: Transformational Approaches in Teaching and Learning appeals to both instructors and professionals, while offering case studies from a wide variety of activities that have been developed around the globe: the creation of entirely new courses and degree programs; tools for K-20 learners, teachers, and the general public; and in-depth analysis of selected programs. As network-based pedagogy and the community of practice continues to grow, we hope that the book's readers will join this vibrant network education community to build on these nascent ideas and help deepen the understanding of networks for all learners.

Generally, spontaneous pattern formation phenomena are random and repetitive, whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both self-organized and architectural. This book is the first initiative of its kind toward establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of "self-architecturing" systems. Particular emphasis is placed on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science-while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies. Altogether, the aim of this work is to provide a framework for and examples of a larger class of "self-architecturing" systems, while addressing fundamental questions such as > How do biological organisms carry out morphogenetic tasks so reliably? > Can we extrapolate their self-formation capabilities to engineered systems? > Can physical systems be endowed with information (or informational systems be embedded in physics) so as to create autonomous morphologies and functions? > What are the core principles and best practices for the design and engineering of such morphogenetic systems? The intended audience consists of researchers and graduate students who are working on, starting to work on, or interested in programmable self-organizing systems in a wide range of scientific fields, including computer science, robotics, bioengineering, control engineering, physics, theoretical biology, mathematics, and many others.

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.