Róbert Ormándi

741 citations
20 papers · 227 · h-index 7

Impact in

Papers in

Róbert Ormándi

19 papers receiving 209 citations

Peers

Róbert Ormándi
Comparison fields: 5 of 41
  • Computer Science Applications 31
  • Artificial Intelligence 153
  • Computer Networks and Communications 81
  • Management Science and Operations Research 38
  • Information Systems 42
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Honglu Jiang United States
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Seung-won Hwang South Korea
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Makbule Gülçin Özsoy Türkiye
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Citations per field
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Citations per year

Countries citing papers authored by Róbert Ormándi

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 13 scholars most cited alongside Róbert Ormándi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Róbert Ormándi Line = papers co-authored together Róbert Ormándi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2012100
2 200923
3
Gossip-based distributed stochastic bandit algorithms
201322
4 201616
5 201013
6 201112
7 20087
8
Scalable Multidimensional Hierarchical Bayesian modeling on Spark
20154
9 20124
10 20104
11 20083
12
Lightning Fast Asynchronous Distributed K-Means Clustering
20143
13 20073
14 20083
15 20132
16 20162
17
Efficient P2P Ensemble Learning with Linear Models on Fully Distributed Data
20112
18 20122
19
Novel balanced feature representation for wikipedia vandalism detection task: Lab report for PAN at CLEF 2010
20101
20 20121

About Róbert Ormándi

Róbert Ormándi is a scholar working on Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Information Systems and Computer Science Applications, having authored 20 papers that have together received 227 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (9 papers), Natural Language Processing Techniques (5 papers), Advanced Bandit Algorithms Research (5 papers), Caching and Content Delivery (5 papers), Topic Modeling (4 papers), Peer-to-Peer Network Technologies (4 papers), Recommender Systems and Techniques (3 papers) and Mobile Crowdsensing and Crowdsourcing (3 papers). The work is most often cited by research in Computer Science Applications (31 citations), Artificial Intelligence (153 citations), Computer Networks and Communications (81 citations), Management Science and Operations Research (38 citations) and Information Systems (42 citations). Róbert Ormándi has collaborated with scholars based in Hungary, United States and France. Frequent co-authors include István Hegedűs, Márk Jelasity, Róbert Busa‐Fekete, György Szarvas, Richárd Farkas, Veronika Vincze, Balázs Szörényi, Balázs Kégl, Datong Chen and Qiang Ma. Their work appears in journals such as Journal of the American Medical Informatics Association, Applied Stochastic Models in Business and Industry, Advances in Complex Systems, Language Resources and Evaluation and Lecture notes in computer science.

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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