arXiv subjects
Publications and source records attributed to Gabor Csardi.
We present a simple stochastic agent-based community finding algorithm. Our algorithm is tested on network data from the Zachary karate club study, data from Victor Hugo's "Les Miserables", and data obtained from a musical piece by J.S. Bach. In all three cases, the algorithm partitions the vertices of the graph sensibly.
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.
This paper reports results of a network theory approach to the study of the United States patent system. We model the patent citation network as a discrete time, discrete space stochastic dynamic system. From data on more than 2 million patents and their citations, we extract an attractiveness function, $A(k,l)$, which determines the likelihood that a patent will be cited. $A(k,l)$ is approximately separable into a product of a function $A_k(k)$ and a function $A_l(l)$, where $k$ is the number of citations already received (in-degree) and $l$ is the age measured in patent number units. $A_l(l)$ displays a peak at low $l$ and a long power law tail, suggesting that some patented technologies have very long-term effects. $A_k(k)$ exhibits super-linear preferential attachment. The preferential attachment exponent has been increasing since 1991, suggesting that patent citations are increasingly concentrated on a relatively small number of patents. The overall average probability that a new patent will be cited by a given patent has increased slightly during the same period. We discuss some possible implications of our results for patent policy.
In this paper we study the resilience of peer-to-peer networks to preferential attacks. We define a network model and experiment with three di erent simple repairing algorithms, out of which the so called 2nd neighbor rewiring algorithm is found to be e ective and plausible for keeping a large connected component in the network, in spite of the continuous attacks. While our motivation comes from peer-to-peer file sharing networks, we believe that our results are more general and applicable in a wide range of networks. All this work was done as a student project in the Complex Systems Summer School 2004, organized by the Santa Fe Institute in Santa Fe, NM, USA.
In this study we introduce and analyze the statistical structural properties of a model of growing networks which may be relevant to social networks. At each step a new node is added which selects 'k' possible partners from the existing network and joins them with probability delta by undirected edges. The 'activity' of the node ends here; it will get new partners only if it is selected by a newcomer. The model produces an infinite-order phase transition when a giant component appears at a specific value of delta, which depends on k. The average component size is discontinuous at the transition. In contrast, the network behaves significantly different for k=1. There is no giant component formed for any delta and thus in this sense there is no phase transition. However, the average component size diverges for delta greater or equal than one half.