Activity maxima in some models of information networks with random weights and heavy tails

Activity maxima in some models of information networks with random weights and heavy tails We consider models of information networks described by random graphs and hypergraphs where each node has a random information activity with distribution having a heavy (regularly varying) tail. We derive sufficient conditions under which the maximum of the aggregate activities (over a node and its neighbors or over communities) asymptotically grows in the same way as the maximum 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 some models of information networks with random weights and heavy tails

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Publisher
Pleiades Publishing
Copyright
Copyright © 2015 by Pleiades Publishing, Inc.
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/S003294601501007X
Publisher site
See Article on Publisher Site

Abstract

We consider models of information networks described by random graphs and hypergraphs where each node has a random information activity with distribution having a heavy (regularly varying) tail. We derive sufficient conditions under which the maximum of the aggregate activities (over a node and its neighbors or over communities) asymptotically grows in the same way as the maximum of individual activities and the Fréchet limit law holds for them.

Journal

Problems of Information TransmissionSpringer Journals

Published: Apr 16, 2015

References

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