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

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This project is 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 decade 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 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 project are to establish a generalized modeling framework that can 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 are used as a means of unified representation of state transition and topological transformation. Network evolution is formulated in two parts, extraction and replacement of subnetworks. For each part, algorithms for automatic rule discovery are explored and developed. Their effectiveness is evaluated through application to real-world network data. This project will produce a novel theoretical framework and a computational toolkit that will transform the ways of studying the dynamics on and of complex networks.

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 is expected to serve as a generalized conceptual/mathematical "language" for modeling, analyzing and discussing the dynamics of various complex systems, which will galvanize 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, Collective Dynamics of Complex Systems Research Group
Associate Professor, Departments of Bioengineering & 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) 777-4439
Fax: (607) 777-5780


© Copyright 2011-2013 Collective Dynamics of Complex Systems Research Group, Binghamton University