Francesco Piccinno

36 total papers · 1.5k total citations
16 papers, 236 citations indexed

About

Francesco Piccinno is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Francesco Piccinno has authored 16 papers receiving a total of 236 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in Francesco Piccinno's work include Natural Language Processing Techniques (11 papers), Topic Modeling (11 papers) and Data Quality and Management (2 papers). Francesco Piccinno is often cited by papers focused on Natural Language Processing Techniques (11 papers), Topic Modeling (11 papers) and Data Quality and Management (2 papers). Francesco Piccinno collaborates with scholars based in Italy, United States and Switzerland. Francesco Piccinno's co-authors include Paolo Ferragina, Yasemin Altün, Peter Shaw, Julian Martin Eisenschlos, Roberto Santoro, Philip Massey, Nigel Collier, Paweł Krzysztof Nowak, Fangyu Liu and Chenxi Pang and has published in prestigious journals such as Computational Intelligence, Infoscience (Ecole Polytechnique Fédérale de Lausanne) and Cineca Institutional Research Information System (Tor Vergata University).

In The Last Decade

Francesco Piccinno

15 papers receiving 219 citations

Author Peers

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

Author Last Decade Papers Cites
Francesco Piccinno 198 45 39 29 14 16 236
Peilin Zhou 144 0.7× 33 0.7× 80 2.1× 16 0.6× 9 0.6× 22 224
David Uthus 188 0.9× 31 0.7× 41 1.1× 20 0.7× 3 0.2× 13 245
Sagnik Ray Choudhury 134 0.7× 98 2.2× 73 1.9× 25 0.9× 6 0.4× 18 250
Shehzaad Dhuliawala 153 0.8× 28 0.6× 24 0.6× 10 0.3× 7 0.5× 14 184
Jen‐Yuan Yeh 258 1.3× 23 0.5× 69 1.8× 11 0.4× 7 0.5× 15 322
Shangwen Lv 283 1.4× 55 1.2× 73 1.9× 16 0.6× 4 0.3× 11 307
Pamela Forner 270 1.4× 44 1.0× 63 1.6× 13 0.4× 8 0.6× 18 320
Sebastian Spiegler 199 1.0× 30 0.7× 60 1.5× 12 0.4× 4 0.3× 18 270
Zhanming Jie 251 1.3× 42 0.9× 37 0.9× 33 1.1× 3 0.2× 15 308
Monica Lestari Paramita 149 0.8× 37 0.8× 110 2.8× 14 0.5× 9 0.6× 19 246

Countries citing papers authored by Francesco Piccinno

Since Specialization
Citations

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

Fields of papers citing papers by Francesco Piccinno

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesco Piccinno

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Piccinno. A scholar is included among the top collaborators of Francesco Piccinno 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 Francesco Piccinno. Francesco Piccinno 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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