Using graph concepts to understand the organization of complex systems

Abstract

Complex networks are universal, arising in fields as disparate as sociology, physics, and biology. In the past decade, extensive research into the properties and behaviors of complex systems has uncovered surprising commonalities among the topologies of different systems. Attempts to explain these similarities have led to the ongoing development and refinement of network models and graph-theoretical analysis techniques with which to characterize and understand complexity. In this tutorial, we demonstrate through illustrative examples, how network measures and models have contributed to the elucidation of the organization of complex systems.

v(1) 38 pages, 7 figures, to appear in the International Journal of Bifurcation and Chaos v(2) Line spacing changed; now 23 pages, 7 figures, to appear in the Special Issue "Complex Networks' Structure and Dynamics'' of the International Journal of Bifurcation and Chaos (Volume 17, Issue 7, July 2007) edited by S. Boccaletti and V. Latora

References in corpus (2)

• Uncovering the overlapping community structure of complex networks in nature and society
• Subnetwork hierarchies of biochemical pathways

Cited by in corpus (5)

• Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier
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• Dynamical and Structural Modularity of Discrete Regulatory Networks
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• Community Structure and Topical Differentiation in European RTD Collaborations