Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A survey of methods for explaining black box models
20192.4k citationsRiccardo Guidotti, Anna Monreale et al.ISTI Open Portalprofile →
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
2023687 citationsSajid Ali, Tamer Abuhmed et al.Information Fusionprofile →
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
2024172 citationsLuca Longo, Mario Brčić et al.Information Fusionprofile →
Counterfactual explanations and how to find them: literature review and benchmarking
2022165 citationsRiccardo GuidottiData Mining and Knowledge Discoveryprofile →
Benchmarking and survey of explanation methods for black box models
202394 citationsFosca Giannotti, Riccardo Guidotti et al.Data Mining and Knowledge Discoveryprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Riccardo Guidotti
Since
Specialization
Citations
This map shows the geographic impact of Riccardo Guidotti'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 Riccardo Guidotti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Riccardo Guidotti more than expected).
Fields of papers citing papers by Riccardo Guidotti
This network shows the impact of papers produced by Riccardo Guidotti. 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 Riccardo Guidotti. The network helps show where Riccardo Guidotti may publish in the future.
Co-authorship network of co-authors of Riccardo Guidotti
This figure shows the co-authorship network connecting the top 25 collaborators of Riccardo Guidotti.
A scholar is included among the top collaborators of Riccardo Guidotti 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 Riccardo Guidotti. Riccardo Guidotti is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Longo, Luca, Mario Brčić, Federico Cabitza, et al.. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion. 106. 102301–102301.172 indexed citations breakdown →
5.
Guidotti, Riccardo, et al.. (2024). Generative Model for Decision Trees. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 21116–21124.
Ali, Sajid, Tamer Abuhmed, Shaker El–Sappagh, et al.. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion. 99. 101805–101805.687 indexed citations breakdown →
Guidotti, Riccardo, et al.. (2020). Data-Driven Location Annotation for Fleet Mobility Modeling.. CINECA IRIS Institutial research information system (University of Pisa). 2020.1 indexed citations
13.
Andrienko, Gennady, Natalia Andrienko, Chiara Boldrini, et al.. (2020). (So) Big Data and the transformation of the city. International Journal of Data Science and Analytics. 11(4). 311–340.19 indexed citations
14.
Guidotti, Riccardo, et al.. (2020). Self-Adapting Trajectory Segmentation. CINECA IRIS Institutial research information system (University of Pisa). 2020.2 indexed citations
15.
Sîrbu, Alina, Gennady Andrienko, Natalia Andrienko, et al.. (2020). Human migration: the big data perspective. International Journal of Data Science and Analytics. 11(4). 341–360.56 indexed citations
16.
Guidotti, Riccardo & Salvatore Ruggieri. (2019). On the Stability of Interpretable Models. CINECA IRIS Institutial research information system (University of Pisa).27 indexed citations
17.
Guidotti, Riccardo, Anna Monreale, Salvatore Ruggieri, et al.. (2019). A survey of methods for explaining black box models. ISTI Open Portal.2443 indexed citations breakdown →
18.
Guidotti, Riccardo, Anna Monreale, & Dino Pedreschi. (2019). The AI Black Box Explanation Problem.. ERCIM news/ERCIM news online edition. 2019.8 indexed citations
19.
Pedreschi, Dino, Fosca Giannotti, Riccardo Guidotti, et al.. (2019). Meaningful Explanations of Black Box AI Decision Systems. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 9780–9784.104 indexed citations
20.
Guidotti, Riccardo, Roberto Trasarti, & Mirco Nanni. (2015). TOSCA. CINECA IRIS Institutial research information system (University of Pisa). 1–10.23 indexed citations
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.