Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications (Extended Version)
Abstract
Although the "scale-free: literature is large and growing, it gives neither a precise definition of scale-free graphs nor rigorous proofs of many of their claimed properties. In fact, it is easily shown that the existing theory has many inherent contradictions and verifiably false claims. In this paper, we propose a new, mathematically precise, and structural definition of the extent to which a graph is scale-free, and prove a series of results that recover many of the claimed properties while suggesting the potential for a rich and interesting theory. With this definition, scale-free (or its opposite, scale-rich) is closely related to other structural graph properties such as various notions of self-similarity (or respectively, self-dissimilarity). Scale-free graphs are also shown to be the likely outcome of random construction processes, consistent with the heuristic definitions implicit in existing random graph approaches. Our approach clarifies much of the confusion surrounding the sensational qualitative claims in the scale-free literature, and offers rigorous and quantitative alternatives.
Additional Information
The primary version of this paper is forthcoming from Internet Mathematics, 2005. The authors are indebted to several colleagues for ongoing conversations and valuable feedback, particularly David Aldous, Jean Carlson, Steven Low, Chris Magee, Matt Roughan, Stanislav Shalunov. This work was supported by Boeing, AFOSR URI 49620-01-1-0365 "Architectures for Secure and Robust Distributed Infrastructures", the Army Institute for Collaborative Biotechnologies, NSF Award: CCF-0326635 "ITR COLLAB: Theory and Software Infrastructure for a Scalable Systems Biology," AFOSR Award: FA9550-05-1-0032 "Bio Inspired Networks," and Caltech's Lee Center for Advanced Networking. Parts of this work were done at the Institute of Pure and Applied Mathematics (IPAM) at UCLA as part of the 2002 annual program on "Large-scale communication networks."Attached Files
Submitted - 0501169.pdf
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Additional details
- Eprint ID
- 96777
- Resolver ID
- CaltechAUTHORS:20190627-100025027
- Boeing Corporation
- Air Force Office of Scientific Research (AFOSR)
- 49620-01-1-0365
- NSF
- CCF-0326635
- Air Force Office of Scientific Research (AFOSR)
- FA9550-05-1-0032
- Caltech Lee Center for Advanced Networking
- Created
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2019-06-27Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field