Adriano Barra
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- Complex Network Analysis Techniques 15
- Statistical Mechanics and Entropy 8
- Model Reduction and Neural Networks 8
- Condensed Matter Physics top 5%
- Theoretical and Computational Physics 33
- Genetics top 5%
- Artificial Intelligence top 5%
- Neural Networks and Applications 27
- Statistics and Probability top 5%
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- Complex Systems and Time Series Analysis 16
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- Neural dynamics and brain function 15
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- Gene Regulatory Network Analysis 10
- Co-authors
- Elena AgliariDaniele TantariFrancesco GuerraGiuseppe GenoveseBrunella FrancoPierluigi ContucciPeter SollichMaria Immacolata Ferrante
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Physical Review Letters (4 papers)Nature Genetics (1 paper)
- Partner nations
- ItalyUnited KingdomUnited States
In The Last Decade
Adriano Barra
97 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 149
- Statistical and Nonlinear Physics 414
- Condensed Matter Physics 337
- Genetics 400
- Artificial Intelligence 427
- Statistics and Probability 102
Countries citing papers authored by Adriano Barra
This map shows the geographic impact of Adriano Barra'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 Adriano Barra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adriano Barra more than expected).
Fields of papers citing papers by Adriano Barra
This network shows the impact of papers produced by Adriano Barra. 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 Adriano Barra. The network helps show where Adriano Barra may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Adriano Barra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 4 | |
| 4 | 2023 | 9 | |
| 5 | 2022 | 7 | |
| 6 | 2022 | 12 | |
| 7 | 2022 | 10 | |
| 8 | 2020 | 15 | |
| 9 | 2018 | 73 | |
| 10 | 2018 | 53 | |
| 11 | 2017 | 38 | |
| 12 | 2015 | 9 | |
| 13 | 2014 | 14 | |
| 14 | 2014 | 43 | |
| 15 | Integration indicators in immigration phenomena. A statistical mechanics perspective | 2013 | 1 |
| 16 | 2013 | 4 | |
| 17 | How much glassy are neural networks | 2012 | 1 |
| 18 | 2012 | 81 | |
| 19 | 2010 | 42 | |
| 20 | 2008 | 65 |
About Adriano Barra
Adriano Barra is a scholar working on Statistical and Nonlinear Physics, Condensed Matter Physics and Artificial Intelligence, having authored 98 papers that have together received 2.2k indexed citations. Recurring topics across this work include Theoretical and Computational Physics (33 papers), Neural Networks and Applications (27 papers), Complex Systems and Time Series Analysis (16 papers), Neural dynamics and brain function (15 papers), Complex Network Analysis Techniques (15 papers), Gene Regulatory Network Analysis (10 papers), Statistical Mechanics and Entropy (8 papers) and Model Reduction and Neural Networks (8 papers). The work is most often cited by research in Statistical and Nonlinear Physics (414 citations), Condensed Matter Physics (337 citations) and Genetics (400 citations). Adriano Barra has collaborated with scholars based in Italy, United Kingdom and United States. Frequent co-authors include Elena Agliari, Daniele Tantari, Francesco Guerra, Giuseppe Genovese, Brunella Franco, Pierluigi Contucci, Peter Sollich, Maria Immacolata Ferrante, Alessandro Zullo and Nadia Messaddeq. Their work appears in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nature Genetics.
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.