Gianluca Amato

73 total papers · 820 total citations
34 papers, 485 citations indexed

About

Gianluca Amato is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Gianluca Amato has authored 34 papers receiving a total of 485 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 12 papers in Computational Theory and Mathematics and 10 papers in Cellular and Molecular Neuroscience. Recurrent topics in Gianluca Amato's work include Logic, programming, and type systems (13 papers), Formal Methods in Verification (9 papers) and Nerve injury and regeneration (7 papers). Gianluca Amato is often cited by papers focused on Logic, programming, and type systems (13 papers), Formal Methods in Verification (9 papers) and Nerve injury and regeneration (7 papers). Gianluca Amato collaborates with scholars based in Italy, United States and Denmark. Gianluca Amato's co-authors include Simona Capsoni, Antonino Cattaneo, Domenico Vignone, Francesca Scozzari, Cecilia Tiveron, Marcello Ceci, Sara Marinelli, Francesca Paoletti, Flaminia Pavone and Sonia Covaceuszach and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Gianluca Amato

31 papers receiving 477 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Gianluca Amato 236 193 146 64 60 34 485
Sreetama Basu 81 0.3× 108 0.6× 145 1.0× 21 0.3× 14 0.2× 19 428
Andrew J. Payne 121 0.5× 106 0.5× 221 1.5× 55 0.9× 10 0.2× 20 483
Shohreh Majd 94 0.4× 200 1.0× 204 1.4× 46 0.7× 35 0.6× 19 435
Akihiko Takashima 110 0.5× 263 1.4× 166 1.1× 73 1.1× 8 0.1× 33 410
Yuanyuan Hou 178 0.8× 70 0.4× 161 1.1× 17 0.3× 25 0.4× 38 448
Tapan K. Khan 45 0.2× 201 1.0× 155 1.1× 99 1.5× 14 0.2× 13 431
Tatjana Petrov 92 0.4× 88 0.5× 132 0.9× 9 0.1× 18 0.3× 32 425
Filippo Ugolini 108 0.5× 100 0.5× 128 0.9× 23 0.4× 43 0.7× 35 516
Ayush Noori 52 0.2× 147 0.8× 162 1.1× 12 0.2× 26 0.4× 26 478
Fuchen Liu 73 0.3× 48 0.2× 246 1.7× 34 0.5× 21 0.3× 30 527

Countries citing papers authored by Gianluca Amato

Since Specialization
Citations

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

Fields of papers citing papers by Gianluca Amato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gianluca Amato

This figure shows the co-authorship network connecting the top 25 collaborators of Gianluca Amato. A scholar is included among the top collaborators of Gianluca Amato 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 Gianluca Amato. Gianluca Amato is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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