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CDI-Type I: Modeling and Predicting State-Topology Coevolution of Complex Adaptive Networks

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This project was supported by the NSF Cyber-enabled Discovery and Innovation Program (Award #: NSF BCS-1027752).
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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About the Project

The rapidly growing complex network science has presented novel approaches to complex systems modeling that were not fully foreseen even in a few decades ago. It addresses the self-organization of complex network structure and its implications for system behavior, which holds significant cross-disciplinary relevance to many fields of natural and social sciences, particularly in today's highly networked social/political/economical circumstances.

Interestingly, complex network science has traditionally addressed either "dynamics on networks" (state transition on a network with a fixed topology) or "dynamics of networks" (topological transformation of a network with no dynamic state changes) almost separately. In many real-world complex biological and social networks, however, these two dynamics interact with each other and often coevolve over the same time scales. Modeling and predicting state-topology coevolution is now recognized as one of the most significant challenges in complex network science.

The goals of this NSF-funded project were to establish a generalized modeling framework that could effectively describe state-topology coevolution of complex adaptive networks and to develop computational methods for automatic discovery of dynamical rules that best capture both state transition and topological transformation in empirical data. To achieve these goals, graph rewriting systems were used as a means of unified representation of state transition and topological transformation. Network evolution was formulated in two parts, extraction and replacement of subnetworks. For each part, algorithms for automatic rule discovery were explored and developed. Their effectiveness was evaluated through application to simulated and real-world network data. This project has produced a novel theoretical framework and a computational toolkit that are expected to become the basis of transformational ways of studying the coevolution of dynamics on and of complex networks in the coming years.

The outcomes of this project have been disseminated via various channels and integrated in multiple educational programs at Binghamton University and other institutions. The developed algorithms and software tools are made freely available to researchers and other professionals for their own use. The developed framework has served as a generalized conceptual/mathematical "language" for modeling, analyzing and discussing the dynamics of various complex systems, which has galvanized interdisciplinary discussion and collaboration across many different areas of applications.


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Contact Us

Please address any inquiries about this project to:

Hiroki Sayama, D.Sc.
Director, Center for Collective Dynamics of Complex Systems
Associate Professor, Department of Systems Science and Industrial Engineering
Binghamton University, State University of New York
P.O. Box 6000, Binghamton, NY 13902-6000
Email: sayama@binghamton.edu
Tel: (607) 777-3566

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