Géza Ódor

8.3k total citations · 1 hit paper
102 papers, 2.0k citations indexed

About

Géza Ódor is a scholar working on Condensed Matter Physics, Mathematical Physics and Statistical and Nonlinear Physics. According to data from OpenAlex, Géza Ódor has authored 102 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Condensed Matter Physics, 49 papers in Mathematical Physics and 39 papers in Statistical and Nonlinear Physics. Recurrent topics in Géza Ódor's work include Theoretical and Computational Physics (75 papers), Stochastic processes and statistical mechanics (49 papers) and Complex Network Analysis Techniques (30 papers). Géza Ódor is often cited by papers focused on Theoretical and Computational Physics (75 papers), Stochastic processes and statistical mechanics (49 papers) and Complex Network Analysis Techniques (30 papers). Géza Ódor collaborates with scholars based in Hungary, Germany and Spain. Géza Ódor's co-authors include N. Menyhárd, Jeffrey Kelling, Róbert Juhász, Claudio Castellano, Miguel A. Muñoz, Haye Hinrichsen, B. Hartmann, Bartosz Liedke, Michael T. Gastner and Attila Szolnoki and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Reviews of Modern Physics.

In The Last Decade

Géza Ódor

97 papers receiving 2.0k citations

Hit Papers

Universality classes in nonequilibrium lattice systems 2004 2026 2011 2018 2004 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Géza Ódor Hungary 21 1.3k 888 765 391 235 102 2.0k
Hyunggyu Park South Korea 25 913 0.7× 534 0.6× 1.0k 1.4× 297 0.8× 175 0.7× 99 1.9k
Tânia Tomé Brazil 22 877 0.7× 379 0.4× 1.1k 1.4× 264 0.7× 109 0.5× 83 1.8k
Joaquín Marro Spain 25 2.0k 1.6× 842 0.9× 1.4k 1.9× 1.2k 3.2× 363 1.5× 161 3.6k
Bernard Derrida France 18 756 0.6× 395 0.4× 500 0.7× 237 0.6× 166 0.7× 23 1.7k
Haye Hinrichsen Germany 27 2.1k 1.7× 1.4k 1.6× 1.2k 1.6× 855 2.2× 78 0.3× 102 3.3k
Christian Maes Belgium 30 1.2k 0.9× 709 0.8× 2.3k 3.0× 366 0.9× 239 1.0× 166 3.4k
H.-J. Sommers Germany 24 981 0.8× 277 0.3× 1.1k 1.5× 450 1.2× 453 1.9× 33 2.4k
J.M. López Spain 21 1.0k 0.8× 322 0.4× 587 0.8× 519 1.3× 39 0.2× 76 2.0k
Miguel A. Rodríguez Spain 25 791 0.6× 258 0.3× 676 0.9× 343 0.9× 50 0.2× 125 2.4k
Raffaella Burioni Italy 23 497 0.4× 303 0.3× 842 1.1× 125 0.3× 284 1.2× 110 1.7k

Countries citing papers authored by Géza Ódor

Since Specialization
Citations

This map shows the geographic impact of Géza Ódor's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Géza Ódor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Géza Ódor more than expected).

Fields of papers citing papers by Géza Ódor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Géza Ódor. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Géza Ódor. The network helps show where Géza Ódor may publish in the future.

Co-authorship network of co-authors of Géza Ódor

This figure shows the co-authorship network connecting the top 25 collaborators of Géza Ódor. A scholar is included among the top collaborators of Géza Ódor based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Géza Ódor. Géza Ódor is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hartmann, B., et al.. (2025). Studying power-grid synchronization with incremental refinement of model heterogeneity. Chaos An Interdisciplinary Journal of Nonlinear Science. 35(1). 1 indexed citations
2.
Ódor, Géza, et al.. (2025). Scale-free behavior of weight distributions of connectomes. Physical Review Research. 7(1). 5 indexed citations
3.
Ódor, Géza, et al.. (2024). Chimera-like states in neural networks and power systems. Chaos An Interdisciplinary Journal of Nonlinear Science. 34(3). 5 indexed citations
4.
Ódor, Géza, et al.. (2024). Improving power-grid systems via topological changes or how self-organized criticality can help power grids. Physical Review Research. 6(1). 4 indexed citations
5.
Ódor, Géza, et al.. (2023). Synchronization transitions on connectome graphs with external force. Frontiers in Physics. 11. 5 indexed citations
6.
Ódor, Géza. (2021). Nonuniversal power-law dynamics of susceptible infected recovered models on hierarchical modular networks. Physical review. E. 103(6). 62112–62112. 6 indexed citations
7.
Ódor, Géza. (2019). Robustness of Griffiths effects in homeostatic connectome models. Physical review. E. 99(1). 12113–12113. 10 indexed citations
8.
Cota, Wesley, Géza Ódor, & Silvio C. Ferreira. (2018). Griffiths phases in infinite-dimensional, non-hierarchical modular networks. Repository of the Academy's Library (Library of the Hungarian Academy of Sciences). 16 indexed citations
9.
Ódor, Géza & B. Hartmann. (2018). Heterogeneity effects in power grid network models. Physical review. E. 98(2). 22305–22305. 26 indexed citations
10.
Cota, Wesley, Silvio C. Ferreira, & Géza Ódor. (2016). Griffiths effects of the susceptible-infected-susceptible epidemic model on random power-law networks. Physical review. E. 93(3). 32322–32322. 23 indexed citations
11.
Ódor, Géza. (2013). Rare regions of the susceptible-infected-susceptible model on Barabási-Albert networks. Physical Review E. 87(4). 42132–42132. 15 indexed citations
12.
Juhász, Róbert, Géza Ódor, Claudio Castellano, & Miguel A. Muñoz. (2012). Rare-region effects in the contact process on networks. Physical Review E. 85(6). 66125–66125. 30 indexed citations
13.
Ódor, Géza & Romualdo Pastor‐Satorras. (2012). Slow dynamics and rare-region effects in the contact process on weighted tree networks. Physical Review E. 86(2). 26117–26117. 19 indexed citations
14.
Ódor, Géza, Róbert Juhász, Claudio Castellano, & Miguel A. Muñoz. (2011). Griffiths phases in the contact process on complex networks. AIP conference proceedings. 172–178. 2 indexed citations
15.
Ódor, Géza, et al.. (2010). Directedd-mer diffusion describing the Kardar-Parisi-Zhang-type surface growth. Physical Review E. 81(3). 31112–31112. 26 indexed citations
16.
Ódor, Géza & N. Menyhárd. (2008). Crossovers from parity conserving to directed percolation universality. Physical Review E. 78(4). 41112–41112. 7 indexed citations
17.
Ódor, Géza & N. Menyhárd. (2006). Critical behavior of an even-offspringed branching and annihilating random-walk cellular automaton with spatial disorder. Physical Review E. 73(3). 36130–36130. 23 indexed citations
18.
Ódor, Géza. (2003). Critical behavior of the one-dimensional diffusive pair contact process. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 67(1). 16111–16111. 29 indexed citations
19.
Menyhárd, N. & Géza Ódor. (2003). Multispecies annihilating random walk transition at zero branching rate: Cluster scaling behavior in a spin model. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 68(5). 56106–56106. 7 indexed citations
20.
Ódor, Géza. (2002). Phase transition universality classes of classical, nonequilibrium systems. arXiv (Cornell University). 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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