Péter Pollner

2.4k total citations
54 papers, 311 citations indexed

About

Péter Pollner is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Péter Pollner has authored 54 papers receiving a total of 311 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Statistical and Nonlinear Physics, 12 papers in Artificial Intelligence and 10 papers in Molecular Biology. Recurrent topics in Péter Pollner's work include Complex Network Analysis Techniques (10 papers), Theoretical and Computational Physics (6 papers) and Opinion Dynamics and Social Influence (6 papers). Péter Pollner is often cited by papers focused on Complex Network Analysis Techniques (10 papers), Theoretical and Computational Physics (6 papers) and Opinion Dynamics and Social Influence (6 papers). Péter Pollner collaborates with scholars based in Hungary, Denmark and United Kingdom. Péter Pollner's co-authors include Gergely Palla, Tamás Vicsek, Anna Horváth, Illés J. Farkas, Tamás Joó, Imre Derényi, György Surján, István Csabai, Péter Banczerowski and Katalin Molnár and has published in prestigious journals such as Physical Review Letters, PLoS ONE and Scientific Reports.

In The Last Decade

Péter Pollner

50 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Péter Pollner Hungary 10 101 60 34 32 31 54 311
Degang Wu China 13 43 0.4× 24 0.4× 36 1.1× 435 13.6× 23 0.7× 39 985
I. Boukhris Tunisia 8 30 0.3× 67 1.1× 24 0.7× 11 0.3× 8 0.3× 41 200
Björn-Erik Erlandsson Sweden 8 16 0.2× 43 0.7× 21 0.6× 80 2.5× 23 0.7× 16 377
Kyung Hwa Lee South Korea 11 10 0.1× 49 0.8× 53 1.6× 25 0.8× 16 0.5× 21 338
Przemysław Waliszewski Poland 16 54 0.5× 48 0.8× 15 0.4× 216 6.8× 43 1.4× 40 562
Gustavo Gomes Resende Brazil 9 30 0.3× 57 0.9× 20 0.6× 28 0.9× 8 0.3× 18 360
Shu Yan China 12 107 1.1× 11 0.2× 4 0.1× 50 1.6× 15 0.5× 28 336
L. Bernstein United States 13 138 1.4× 20 0.3× 10 0.3× 173 5.4× 41 1.3× 20 536
M Giordano Italy 8 19 0.2× 14 0.2× 53 1.6× 9 0.3× 17 0.5× 21 278
Thomas Aschenbrenner Germany 8 63 0.6× 40 0.7× 6 0.2× 102 3.2× 7 0.2× 10 347

Countries citing papers authored by Péter Pollner

Since Specialization
Citations

This map shows the geographic impact of Péter Pollner'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 Péter Pollner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Péter Pollner more than expected).

Fields of papers citing papers by Péter Pollner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Péter Pollner. 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 Péter Pollner. The network helps show where Péter Pollner may publish in the future.

Co-authorship network of co-authors of Péter Pollner

This figure shows the co-authorship network connecting the top 25 collaborators of Péter Pollner. A scholar is included among the top collaborators of Péter Pollner 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 Péter Pollner. Péter Pollner 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.
Pollner, Péter, et al.. (2025). Investigating the Performance of Retrieval-Augmented Generation and Domain-Specific Fine-Tuning for the Development of AI-Driven Knowledge-Based Systems. Machine Learning and Knowledge Extraction. 7(1). 15–15. 10 indexed citations
2.
Madaras, Lilla, et al.. (2025). Histopathology and proteomics are synergistic for high-grade serous ovarian cancer platinum response prediction. npj Precision Oncology. 9(1). 27–27. 2 indexed citations
3.
Pollner, Péter, et al.. (2025). Consistency and grade prediction of intracranial meningiomas based on fractal geometry analysis. Neurosurgical Review. 48(1). 598–598.
4.
Palla, Gergely, et al.. (2024). Path of excellence: A co-authorship network analysis of European Research Council grant winners in social sciences. Heliyon. 10(12). e32403–e32403. 1 indexed citations
5.
Szócska, Miklós, Csaba Dobó‐Nagy, Attila Mócsai, et al.. (2024). Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?. Journal of Clinical Medicine. 13(4). 1106–1106. 2 indexed citations
6.
Gordon, Peter G., et al.. (2024). Classification of colorectal primer carcinoma from normal colon with mid‐infrared spectra. Journal of Chemometrics. 38(7).
7.
Pollner, Péter, et al.. (2024). A multimodal deep learning architecture for smoking detection with a small data approach. Frontiers in Artificial Intelligence. 7. 1326050–1326050. 4 indexed citations
8.
Pollner, Péter, et al.. (2024). Transfer learning may explain pigeons’ ability to detect cancer in histopathology. Bioinspiration & Biomimetics. 19(5). 56016–56016. 1 indexed citations
9.
Csabai, István, et al.. (2024). Annotated dataset for training deep learning models to detect astrocytes in human brain tissue. Scientific Data. 11(1). 96–96. 3 indexed citations
10.
Pollner, Péter, et al.. (2023). Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability. Scientific Reports. 13(1). 4226–4226. 2 indexed citations
11.
Pataki, Bálint, Dezső Ribli, Benedek Gyöngyösi, et al.. (2022). HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening. Scientific Data. 9(1). 370–370. 18 indexed citations
12.
Horváth, Anna, et al.. (2022). A Novel Prognostication System for Spinal Metastasis Patients Based on Network Science and Correlation Analysis. Clinical Oncology. 35(1). e20–e29. 2 indexed citations
13.
Pollner, Péter, et al.. (2022). Peripheral gene interactions define interpretable clusters of core ASD genes in a network-based investigation of the omnigenic theory. npj Systems Biology and Applications. 8(1). 28–28. 4 indexed citations
14.
Aghion, Erez, et al.. (2021). Anomalous diffusion in the citation time series of scientific publications. Journal of Physics Complexity. 2(3). 35024–35024. 4 indexed citations
15.
Kovács, László, et al.. (2021). Networks in the mental lexicon – contributions from Hungarian. 12(2). 107–127. 1 indexed citations
16.
Palla, Gergely, et al.. (2021). Hierarchy and control of ageing-related methylation networks. PLoS Computational Biology. 17(9). e1009327–e1009327. 5 indexed citations
17.
Horváth, Anna, et al.. (2020). Research on the predicting power of the revised Tokuhashi system: how much time can surgery give to patients with short life expectancy?. International Journal of Clinical Oncology. 25(4). 755–764. 3 indexed citations
18.
Pollner, Péter, et al.. (2019). Time evolution of the hierarchical networks between PubMed MeSH terms. PLoS ONE. 14(8). e0220648–e0220648. 7 indexed citations
19.
Rizzo, Tommaso, József Stéger, Péter Pollner, István Csabai, & Gábor Vattay. (2008). High quality queueing information from accelerated active network tomography. 22. 1 indexed citations
20.
Vörös, Z., et al.. (2003). Tunable Lyapunov exponent in inverse magnetic billiards. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 67(6). 65202–65202. 6 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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