Mirko Polato

950 total citations
38 papers, 502 citations indexed

About

Mirko Polato is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mirko Polato has authored 38 papers receiving a total of 502 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 14 papers in Information Systems and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mirko Polato's work include Recommender Systems and Techniques (9 papers), Machine Learning and Data Classification (6 papers) and Privacy-Preserving Technologies in Data (6 papers). Mirko Polato is often cited by papers focused on Recommender Systems and Techniques (9 papers), Machine Learning and Data Classification (6 papers) and Privacy-Preserving Technologies in Data (6 papers). Mirko Polato collaborates with scholars based in Italy, China and Netherlands. Mirko Polato's co-authors include Fabio Aiolli, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni, Alessia Antelmi, Guglielmo Faggioli, Dingqi Yang, Carmine Spagnuolo, Gennaro Cordasco and Vittorio Scarano and has published in prestigious journals such as The Journal of Chemical Physics, IEEE Access and ACM Computing Surveys.

In The Last Decade

Mirko Polato

35 papers receiving 485 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mirko Polato Italy	12	207	163	96	67	66	38	502
Myunggwon Hwang South Korea	11	186 0.9×	109 0.7×	25 0.3×	68 1.0×	34 0.5×	54	370
Junli Wang China	12	304 1.5×	157 1.0×	34 0.4×	74 1.1×	29 0.4×	59	568
Devis Bianchini Italy	13	223 1.1×	325 2.0×	86 0.9×	38 0.6×	31 0.5×	63	531
Manuel Carro Spain	9	222 1.1×	141 0.9×	72 0.8×	39 0.6×	39 0.6×	36	457
Luca Virgili Italy	15	217 1.0×	146 0.9×	23 0.2×	71 1.1×	30 0.5×	45	545
Muhammad Bilal Amin Australia	13	140 0.7×	245 1.5×	27 0.3×	66 1.0×	16 0.2×	29	513
Juri Di Rocco Italy	16	280 1.4×	501 3.1×	83 0.9×	25 0.4×	34 0.5×	70	697
Gabriele Kotsis Austria	12	75 0.4×	127 0.8×	26 0.3×	68 1.0×	28 0.4×	89	424
Mohammad Dabbagh Malaysia	15	121 0.6×	480 2.9×	53 0.6×	27 0.4×	32 0.5×	21	632
E. Sandeep Kumar India	7	206 1.0×	595 3.7×	64 0.7×	75 1.1×	24 0.4×	13	715

Countries citing papers authored by Mirko Polato

Since Specialization

Citations

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

Fields of papers citing papers by Mirko Polato

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mirko Polato

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

All Works

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20 of 20 papers shown

Polato, Mirko, et al.. (2026). Learning in federated and dynamic environments: A tutorial on challenges, trends, and practical strategies. Neurocomputing. 672. 132671–132671.

Kim, Sunwoo, Soo Yong Lee, Yue Gao, et al.. (2025). A Tutorial on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. 6829–6832.

Polato, Mirko, et al.. (2025). SVF: Support Vector Federation. IEEE Access. 13. 77778–77789. 1 indexed citations

Polato, Mirko. (2025). Fluke: Federated learning utility framework for experimentation and research. Future Generation Computer Systems. 177. 108241–108241.

Polato, Mirko, et al.. (2024). PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning. IEEE Transactions on Services Computing. 18(2). 998–1011. 9 indexed citations

Kim, Sunwoo, Soo Yong Lee, Yue Gao, et al.. (2024). A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. 6534–6544. 11 indexed citations

Polato, Mirko, et al.. (2024). Improving rule-based classifiers by Bayes point aggregation. Neurocomputing. 613. 128699–128699. 1 indexed citations

Mittone, Gianluca, Robert Birke, Iacopo Colonnelli, et al.. (2023). Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. CINECA IRIS Institutial research information system (University of Pisa). 73–83. 4 indexed citations

Polato, Mirko, Guglielmo Faggioli, & Fabio Aiolli. (2022). PRL: A game theoretic large margin method for interpretable feature learning. Neurocomputing. 479. 106–120. 2 indexed citations

10.

Fietta, Valentina, et al.. (2021). Dissociation Between Users’ Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study. IEEE Transactions on Human-Machine Systems. 52(3). 481–489. 47 indexed citations

11.

Polato, Mirko, et al.. (2020). Radical scavenging activity of natural antioxidants and drugs: Development of a combined machine learning and quantum chemistry protocol. The Journal of Chemical Physics. 153(11). 17 indexed citations

12.

Faggioli, Guglielmo, Mirko Polato, & Fabio Aiolli. (2020). Recency Aware Collaborative Filtering for Next Basket Recommendation. Research Padua Archive (University of Padua). 80–87. 35 indexed citations

13.

Aiolli, Fabio, Mauro Conti, Ankit Gangwal, & Mirko Polato. (2019). Mind your wallet’s privacy identifying Bitcoin wallet apps and user’s actions through network traffic analysis. 1484–1491. 2 indexed citations

14.

Perracchione, Emma, et al.. (2019). Learning with subsampled kernel-based methods: Environmental and financial applications. Research Padua Archive (University of Padua). 12(1). 2 indexed citations

15.

Faggioli, Guglielmo, Mirko Polato, & Fabio Aiolli. (2019). Tag-Based User Profiling. Padua Research Archive (University of Padova). 267–271. 1 indexed citations

16.

Lauriola, Ivano, Mirko Polato, & Fabio Aiolli. (2018). The minimum effort maximum output principle applied to Multiple Kernel Learning.. Research Padua Archive (University of Padua). 2 indexed citations

17.

Polato, Mirko & Fabio Aiolli. (2018). Boolean kernels for interpretable kernel machines.. Research Padua Archive (University of Padua). 1 indexed citations

18.

Polato, Mirko, Alessandro Sperduti, Andrea Burattin, & Massimiliano de Leoni. (2018). Time and activity sequence prediction of business process instances. Computing. 100(9). 1005–1031. 70 indexed citations

19.

Polato, Mirko, et al.. (2017). Model-free predictive current control for a SynRM drive based on an effective update of measured current responses. Research Padua Archive (University of Padua). 119–124. 30 indexed citations

20.

Aiolli, Fabio & Mirko Polato. (2016). Kernel based collaborative filtering for very large scale top-N item recommendation.. The European Symposium on Artificial Neural Networks. 5 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.

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