Gabor Csardi

Dynamics of citation networks

The aim of this paper is to give theoretical and experimental tools for measuring the driving force in evolving complex networks. First a discrete-time stochastic model framework is introduced to state the question of how the dynamics of these networks depend on the properties of the parts of the system. Then a method is presented to determine this dependence in the possession of the required data about the system. This measurement method is applied to the citation network of high energy physics papers to extract the in-degree and age dependence of the dynamics. It is shown that the method yields close to “optimal” results.

Channel noise in Hodgkin–Huxley model neurons

Abstract We study the effect of channel noise on the firing properties of the Hodgkin–Huxley equations. The spectral properties of the currents and corresponding Hurst indices are determined, and their relevance for information processing is discussed. The interspike interval histograms and their properties are also discussed.

The Inverse Problem of Evolving Networks — with Application to Social Nets

Many complex systems can be modeled by graphs [8]. The vertices of the graph represent objects of the system, and the edges of the graph the relationships between these objects. These relationships may be structural or functional, according to the modeler’s needs [1, 29, 7].

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, that 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.