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a black and white version of this preprint is at http://arxiv.org/abs/cond-mat/0508028, as submitted to Physical Review E (statistical, nonlinear, soft matter physics).

Download the Paper (573 K, PDF file) - August 5, 2005 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
We investigate a simple generative model for network formation. The model is designed to describe the growth of networks of kinship, trading, corporate alliances, or autocatalytic chemical reactions, where feedback is an essential element of network growth. The underlying graphs in these situations grow via a competition between cycle formation and node addition. After choosing a given node, a search is made for another node at a suitable distance. If such a node is found, a link is added connecting this to the original node, and increasing the number of cycles in the graph; if such a node cannot be found, a new node is added, which is linked to the original node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics.

SUGGESTED CITATION:
Douglas R. White, Natasa Kejzar, Constantino Tsallis, Doyne Farmer, and Scott D. White, "A generative model for feedback networks" (August 5, 2005). Social Dynamics and Complexity. Working Papers Series: Paper 20050804.
http://repositories.cdlib.org/imbs/socdyn/wp/20050804