Valeria Nikolaenko
- Artificial Intelligence top 2%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Dan BonehStratis IoannidisUdi WeinsbergNina TaftMarc JóyeLéo DucasIlya MironovMichael Naehrig
- Topics
- Cryptography and Data Security (5 papers)Complexity and Algorithms in Graphs (2 papers)Cellular transport and secretion (2 papers)
- Journals
- Human Molecular GeneticsCommunications BiologyJournal of Mathematical Sciences
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Valeria Nikolaenko
7 papers receiving 574 citations
Peers
Comparison fields: 5 of 56
- Artificial Intelligence 533
- Information Systems 164
- Computer Vision and Pattern Recognition 69
- Computational Theory and Mathematics 66
- Computer Networks and Communications 65
Countries citing papers authored by Valeria Nikolaenko
This map shows the geographic impact of Valeria Nikolaenko'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 Valeria Nikolaenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Valeria Nikolaenko more than expected).
Fields of papers citing papers by Valeria Nikolaenko
This network shows the impact of papers produced by Valeria Nikolaenko. 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 Valeria Nikolaenko. The network helps show where Valeria Nikolaenko may publish in the future.
Co-authorship network of co-authors of Valeria Nikolaenko
This figure shows the co-authorship network connecting the top 25 collaborators of Valeria Nikolaenko. A scholar is included among the top collaborators of Valeria Nikolaenko 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 Valeria Nikolaenko. Valeria Nikolaenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 13 | |
| 4 | 1 | |
| 5 | 133 | |
| 6 | 262 | |
| 7 | 176 | |
| 8 | 1 | |
| 9 | Optimal heuristic algorithms for the image of an injective function. | 0 |
About Valeria Nikolaenko
Valeria Nikolaenko is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Cell Biology, having authored 9 papers that have together received 588 indexed citations. Recurring topics across this work include Cryptography and Data Security (5 papers), Complexity and Algorithms in Graphs (2 papers) and Cellular transport and secretion (2 papers). The work is most often cited by research in Artificial Intelligence (533 citations), Information Systems (164 citations) and Computer Science Applications (37 citations). Valeria Nikolaenko has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Dan Boneh, Stratis Ioannidis, Udi Weinsberg, Nina Taft, Marc Jóye, Léo Ducas, Ilya Mironov, Michael Naehrig, Joppe W. Bos and Ananth Raghunathan. Their work appears in journals such as Human Molecular Genetics, Communications Biology and Journal of Mathematical Sciences.
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.