Fail Gafarov

32 papers receiving 247 citations

Peers

Fail Gafarov
Comparison fields: 5 of 89
  • Statistical and Nonlinear Physics 92
  • Modeling and Simulation 16
  • Economics and Econometrics 95
  • Computer Science Applications 16
  • Cognitive Neuroscience 50
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Yasunori Okabe Japan
L. Romanelli Argentina
Changgui Gu China
Giacomo Raffaelli Italy
Sergey Demin Russia
Frank K. Moss United States
P Boveroux Belgium
Miguel Martı́n-Landrove Venezuela
Paul C. Gailey United States
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Fail Gafarov relative to Yasunori Okabe Japan Yasunori Okabe's profile →
Citations per field
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Citations per year

Countries citing papers authored by Fail Gafarov

Since Specialization
Citations

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

Fields of papers citing papers by Fail Gafarov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Fail Gafarov, 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 Fail Gafarov Line = papers co-authored together Fail Gafarov links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200041
2 200237
3 200127
4 202018
5 201815
6 199914
7 200413
8 200313
9 200312
10 200511
11 200210
12 200210
13 20209
14 20166
15 20064
16 20214
17 19993
18 20093
19 20223
20 20183

About Fail Gafarov

Fail Gafarov is a scholar working on Economics and Econometrics, Cognitive Neuroscience, Artificial Intelligence, Cellular and Molecular Neuroscience and Statistical and Nonlinear Physics, having authored 38 papers that have together received 271 indexed citations. Recurring topics across this work include Complex Systems and Time Series Analysis (12 papers), Neural dynamics and brain function (9 papers), Fractal and DNA sequence analysis (6 papers), Neural Networks and Applications (5 papers), Educational Innovations and Challenges (5 papers), stochastic dynamics and bifurcation (4 papers), Online Learning and Analytics (3 papers) and Nonlinear Dynamics and Pattern Formation (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (92 citations), Modeling and Simulation (16 citations), Economics and Econometrics (95 citations), Computer Science Applications (16 citations) and Cognitive Neuroscience (50 citations). Fail Gafarov has collaborated with scholars based in Russia, Germany and Spain. Frequent co-authors include Renat M. Yulmetyev, Peter Hänggi, Sergey Demin, Р. Р. Нигматуллин, Max Talanov, Francesco M. Noè, Rashid Giniatullin, Andrea Nistri, Ksenia Koroleva and Nikita Mikhailov. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Journal of Integrative Neuroscience, ZooKeys, Neural Networks and Vysshee Obrazovanie v Rossii = Higher Education in Russia.

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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