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
SP MAIK Nauka/Interperiodica
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

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