Estimating the dynamics of kernel-based evolving networks
Disordered Systems and Neural Networks
2009-09-29 v3
Abstract
In this paper we present the application of a novel methodology to scientific citation and collaboration networks. This methodology is designed for understanding the governing dynamics of evolving networks and relies on an attachment kernel, a scalar function of node properties, which stochastically drives the addition and deletion of vertices and edges. We illustrate how the kernel function of a given network can be extracted from the history of the network and discuss other possible applications.
Cite
@article{arxiv.cond-mat/0605497,
title = {Estimating the dynamics of kernel-based evolving networks},
author = {Gabor Csardi and Katherine Strandburg and Laszlo Zalanyi and Jan Tobochnik and Peter Erdi},
journal= {arXiv preprint arXiv:cond-mat/0605497},
year = {2009}
}
Comments
9 pages, 3 figures, 1 table