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.
Problems of Information Transmission – Springer Journals
Published: Jul 11, 2008
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