Csaba Noszály
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Csaba Noszály has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- István Fazekas
Faculty of Informatics, University of Debrecen, Debrecen, Hungary
4 shared publications A. Perecsényi
Univ Debrecen, Fac Informat, POB 400, H-4002 Debrecen, Hungary
1 shared publication- Attila Barta
Univ Debrecen, Fac Informat, Debrecen, Hungary
1 shared publication - Attila Perecsényi
Faculty of Informatics University of Debrecen P.O. Box 12 4010 Debrecen Hungary
1 shared publication - Csaba Vincze
Institute of Mathematics, University of Debrecen, Debrecen, Hungary
1 shared publication - Matyas Barczy
HUN-REN–SZTE Analysis and Applications Research Group, Bolyai Institute, University of Szeged, Szeged, Hungary
1 shared publication
Latest Publications
- 23 July 2020
- Published by Annales Mathematicae et Informaticae - AMI in Annales Mathematicae et Informaticae
- Vol. 51, 7-15
Simulation results on a triangle-based network evolution model
- 23 July 2020
- Published by Annales Mathematicae et Informaticae - AMI in Annales Mathematicae et Informaticae
- Vol. 51, 7-15
Simulation results on a triangle-based network evolution model
- Open Access
- 16 September 2019
- Published by VTeX in Modern Stochastics: Theory and Applications
- Vol. 6 (3) , 311-331
Taylor’s power law for the N-stars network evolution model
Taylor’s power law states that the variance function decays as a power law. It is observed for population densities of species in ecology. For random networks another power law, that is, the power law degree distribution is widely studied. In this paper the original Taylor’s power law is considered for random networks. A precise mathematical proof is presented that Taylor’s power law is asymptotically true for the N-stars network evolution model.
- 16 September 2019
- Published by VTeX in Modern Stochastics: Theory and Applications
- Vol. 6 (3) , 311-331
Open AccessTaylor’s power law for the N-stars network evolution model
Taylor’s power law states that the variance function decays as a power law. It is observed for population densities of species in ecology. For random networks another power law, that is, the power law degree distribution is widely studied. In this paper the original Taylor’s power law is considered for random networks. A precise mathematical proof is presented that Taylor’s power law is asymptotically true for the N-stars network evolution model.
- Open Access
The $N$-stars network evolution model
A new network evolution model is introduced in this paper. The model is based on co-operations of $N$ units. The units are the nodes of the network and the co-operations are indicated by directed links. At each evolution step $N$ units co-operate which formally means that they form a directed $N$-star subgraph. At each step either a new unit joins to the network and it co-operates with $N-1$ old units or $N$ old units co-operate. During the evolution both preferential attachment and uniform choice are applied. Asymptotic power law distributions are obtained both for the in-degrees and the out-degrees.Open AccessThe $N$-stars network evolution model
A new network evolution model is introduced in this paper. The model is based on co-operations of $N$ units. The units are the nodes of the network and the co-operations are indicated by directed links. At each evolution step $N$ units co-operate which formally means that they form a directed $N$-star subgraph. At each step either a new unit joins to the network and it co-operates with $N-1$ old units or $N$ old units co-operate. During the evolution both preferential attachment and uniform choice are applied. Asymptotic power law distributions are obtained both for the in-degrees and the out-degrees. - 1 December 2018
- Published by Elsevier in Statistics & Probability Letters
- Vol. 143, 17-27
A population evolution model and its applications to random networks
- 1 December 2018
- Published by Elsevier in Statistics & Probability Letters
- Vol. 143, 17-27
A population evolution model and its applications to random networks
- Open Access
A Robbins-Monro type algorithm for computing global minimizer of generalized conic functions
We generalize the notion and some properties of the conic function introduced by Vincze and Nagy (2012). We provide a stochastic algorithm for computing the global minimizer of generalized conic functions, we prove almost sure and L^q-convergence of this algorithm.
Open AccessA Robbins-Monro type algorithm for computing global minimizer of generalized conic functions
We generalize the notion and some properties of the conic function introduced by Vincze and Nagy (2012). We provide a stochastic algorithm for computing the global minimizer of generalized conic functions, we prove almost sure and L^q-convergence of this algorithm.
- 1 December 2013
- Published by Akademiai Kiado Zrt. in Studia Scientiarum Mathematicarum Hungarica
- Vol. 50 (4) , 413-422
Empirical results on distance of two-dimensional samples
The distance of two-dimensional samples is studied. The distance of two samples is based on the optimal matching method. Simulation results are obtained when the samples are drawn from normal and uniform distributions.
- 1 December 2013
- Published by Akademiai Kiado Zrt. in Studia Scientiarum Mathematicarum Hungarica
- Vol. 50 (4) , 413-422
Empirical results on distance of two-dimensional samples
The distance of two-dimensional samples is studied. The distance of two samples is based on the optimal matching method. Simulation results are obtained when the samples are drawn from normal and uniform distributions.