Róbert Ormándi
- Artificial Intelligence top 10%
- Computer Networks and Communications
- Management Science and Operations Research
- Information Systems
- Computer Science Applications
- Co-authors
- István HegedűsMárk JelasityRóbert Busa‐FeketeBalázs SzörényiBalázs KéglRichárd FarkasVeronika VinczeGyörgy Szarvas
- Topics
- Data Stream Mining Techniques (6 papers)Caching and Content Delivery (4 papers)Advanced Bandit Algorithms Research (4 papers)
- Journals
- Journal of the American Medical Informatics AssociationLanguage Resources and EvaluationConcurrency and Computation Practice and Experience
- Partner nations
- HungaryUnited StatesGermany
In The Last Decade
Róbert Ormándi
13 papers receiving 154 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 115
- Computer Networks and Communications 58
- Management Science and Operations Research 30
- Information Systems 25
- Computer Science Applications 22
Countries citing papers authored by Róbert Ormándi
This map shows the geographic impact of Róbert Ormándi'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 Róbert Ormándi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Róbert Ormándi more than expected).
Fields of papers citing papers by Róbert Ormándi
This network shows the impact of papers produced by Róbert Ormándi. 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 Róbert Ormándi. The network helps show where Róbert Ormándi may publish in the future.
Co-authorship network of co-authors of Róbert Ormándi
This figure shows the co-authorship network connecting the top 25 collaborators of Róbert Ormándi. A scholar is included among the top collaborators of Róbert Ormándi 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 Róbert Ormándi. Róbert Ormándi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 14 | |
| 3 | Scalable Multidimensional Hierarchical Bayesian modeling on Spark | 3 |
| 4 | Lightning Fast Asynchronous Distributed K-Means Clustering | 2 |
| 5 | Gossip-based distributed stochastic bandit algorithms | 22 |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 2 | |
| 9 | 87 | |
| 10 | Novel balanced feature representation for wikipedia vandalism detection task: Lab report for PAN at CLEF 2010 | 1 |
| 11 | 3 | |
| 12 | 21 | |
| 13 | Hungarian Word-Sense Disambiguated Corpus. | 3 |
| 14 | 3 |
About Róbert Ormándi
Róbert Ormándi is a scholar working on Computational Mathematics, Computer Science Applications and Artificial Intelligence, having authored 14 papers that have together received 167 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (6 papers), Caching and Content Delivery (4 papers) and Advanced Bandit Algorithms Research (4 papers). The work is most often cited by research in Computer Science Applications (22 citations), Artificial Intelligence (115 citations) and Computer Networks and Communications (58 citations). Róbert Ormándi has collaborated with scholars based in Hungary, United States and Germany. Frequent co-authors include István Hegedűs, Márk Jelasity, Róbert Busa‐Fekete, Balázs Szörényi, Balázs Kégl, Richárd Farkas, Veronika Vincze, György Szarvas, Qiang Ma and Datong Chen. Their work appears in journals such as Journal of the American Medical Informatics Association, Language Resources and Evaluation and Concurrency and Computation Practice and Experience.
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.