Mario Pavone

97 total papers · 1.4k total citations
47 papers, 481 citations indexed

About

Mario Pavone is a scholar working on Biomedical Engineering, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Mario Pavone has authored 47 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Biomedical Engineering, 13 papers in Molecular Biology and 13 papers in Artificial Intelligence. Recurrent topics in Mario Pavone's work include Artificial Immune Systems Applications (18 papers), Metaheuristic Optimization Algorithms Research (11 papers) and T-cell and B-cell Immunology (6 papers). Mario Pavone is often cited by papers focused on Artificial Immune Systems Applications (18 papers), Metaheuristic Optimization Algorithms Research (11 papers) and T-cell and B-cell Immunology (6 papers). Mario Pavone collaborates with scholars based in Italy, United Kingdom and Mexico. Mario Pavone's co-authors include Vincenzo Cutello, Giuseppe Nicosia, Jon Timmis, Giuseppe Narzisi, Carlos A. Coello Coello, Natalio Krasnogor, Alireza Osareh, Stephanie Forrest, Kalyanmoy Deb and Bita Shadgar and has published in prestigious journals such as Nucleic Acids Research, IEEE Transactions on Evolutionary Computation and Applied Soft Computing.

In The Last Decade

Mario Pavone

46 papers receiving 450 citations

Author Peers

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

Author						Papers	Cites
Mario Pavone	207	190	173	84	66	47	481
Christine Zarges	99 0.5×	308 1.6×	41 0.2×	60 0.7×	188 2.8×	37	426
Franciszek Seredyński	65 0.3×	201 1.1×	78 0.5×	11 0.1×	183 2.8×	70	507
Emad Nabil	30 0.1×	254 1.3×	69 0.4×	10 0.1×	85 1.3×	30	473
Mingyan Jiang	38 0.2×	184 1.0×	40 0.2×	8 0.1×	39 0.6×	66	484
Wook-Dong Kim	44 0.2×	151 0.8×	203 1.2×	8 0.1×	30 0.5×	39	538
Satish Chandra	31 0.1×	203 1.1×	67 0.4×	7 0.1×	37 0.6×	45	542
Guancheng Wang	76 0.4×	150 0.8×	61 0.4×	7 0.1×	23 0.3×	44	501
Hiroshi Tanaka	66 0.3×	87 0.5×	131 0.8×	6 0.1×	102 1.5×	57	488
Chiwen Qu	44 0.2×	226 1.2×	20 0.1×	5 0.1×	89 1.3×	29	413
Abdulhameed F. Alkhateeb	36 0.2×	45 0.2×	34 0.2×	32 0.4×	67 1.0×	38	490

Countries citing papers authored by Mario Pavone

Since Specialization

Citations

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

Fields of papers citing papers by Mario Pavone

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mario Pavone

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