Activity maxima in random networks in the heavy tail case

Activity maxima in random networks in the heavy tail case We consider a model of information network described by an undirected random graph, where each node has a random information activity whose distribution possesses a heavy tail (with regular variation). We investigate the cases of networks described by classical and power-law random graphs. We derive sufficient conditions under which the maximum of aggregate activities (over a node and its nearest neighbors) asymptotically grows in the same way as the maximium of individual activities and the Fréchet limit law holds for them. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Problems of Information Transmission Springer Journals

Activity maxima in random networks in the heavy tail case

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Publisher
Springer Journals
Copyright
Copyright © 2008 by Pleiades Publishing, Ltd.
Subject
Engineering; Communications Engineering, Networks; Electrical Engineering; Information Storage and Retrieval; Systems Theory, Control
ISSN
0032-9460
eISSN
1608-3253
D.O.I.
10.1134/S0032946008010075
Publisher site
See Article on Publisher Site

Abstract

We consider a model of information network described by an undirected random graph, where each node has a random information activity whose distribution possesses a heavy tail (with regular variation). We investigate the cases of networks described by classical and power-law random graphs. We derive sufficient conditions under which the maximum of aggregate activities (over a node and its nearest neighbors) asymptotically grows in the same way as the maximium of individual activities and the Fréchet limit law holds for them.

Journal

Problems of Information TransmissionSpringer Journals

Published: Jul 11, 2008

References

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