Estimating the dynamics of kernel-based evolving networks
  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. 
Gabor Csardi, Katherine Strandburg, Laszlo Zalanyi, Jan Tobochnik and   Peter Erdi