Estimating the Dynamics of Kernel-Based Evolving Networks

Gabor Csardi, KFKI Research Institute of the Hungarian Academy of Sciences
Katherine J. Strandburg, DePaul University College of Law
Laszlo Zalanyi, KFKI Research Institute of the Hungarian Academy of Sciences
Jan Tobochnik, Kalamazoo College
Peter Erdi, Kalamazoo College

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.