Marco Podda

1.2k total citations
18 papers, 716 citations indexed

About

Marco Podda is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Marco Podda has authored 18 papers receiving a total of 716 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 8 papers in Artificial Intelligence and 5 papers in Computational Theory and Mathematics. Recurrent topics in Marco Podda's work include Advanced Graph Neural Networks (8 papers), Computational Drug Discovery Methods (5 papers) and Topic Modeling (5 papers). Marco Podda is often cited by papers focused on Advanced Graph Neural Networks (8 papers), Computational Drug Discovery Methods (5 papers) and Topic Modeling (5 papers). Marco Podda collaborates with scholars based in Italy, United States and Switzerland. Marco Podda's co-authors include Lester Packer, Alessio Micheli, Davide Bacciu, C. W. Weber, Maret G. Traber, Federico Errica, Heinz Ulrich, Hans Tritschler, Luigi Gagliardi and Roberto Bellù and has published in prestigious journals such as Bioinformatics, Scientific Reports and Biochemical and Biophysical Research Communications.

In The Last Decade

Marco Podda

16 papers receiving 693 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Podda Italy 6 185 183 178 138 92 18 716
Jian Jiang China 24 717 3.9× 177 1.0× 167 0.9× 133 1.0× 26 0.3× 86 1.9k
Joachim Baumann Germany 14 154 0.8× 107 0.6× 98 0.6× 29 0.2× 97 1.1× 42 1.1k
Paulo Matafome Portugal 30 684 3.7× 87 0.5× 162 0.9× 155 1.1× 22 0.2× 103 2.5k
Chaoran Wang China 22 392 2.1× 20 0.1× 82 0.5× 47 0.3× 36 0.4× 107 1.4k
Ihsan Ul Haq Pakistan 21 167 0.9× 67 0.4× 193 1.1× 23 0.2× 45 0.5× 66 1.2k
Yanzhu Liu China 19 120 0.6× 157 0.9× 117 0.7× 53 0.4× 43 0.5× 77 1.3k
Haibo Yang China 19 240 1.3× 31 0.2× 66 0.4× 104 0.8× 14 0.2× 70 1.1k
Yanbin Ye China 15 158 0.9× 43 0.2× 51 0.3× 77 0.6× 14 0.2× 33 707

Countries citing papers authored by Marco Podda

Since Specialization
Citations

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

Fields of papers citing papers by Marco Podda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Podda

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

All Works

18 of 18 papers shown
1.
Podda, Marco, et al.. (2025). Towards Efficient Molecular Property Optimization with Graph Energy Based Models. CINECA IRIS Institutial research information system (University of Pisa). 289–294. 1 indexed citations
2.
Micheli, Alessio, et al.. (2025). Efficient quantification on large-scale networks. Machine Learning. 114(12).
3.
Podda, Marco, Simone Bonechi, Andrea Palladino, et al.. (2024). Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning. iScience. 27(3). 109257–109257. 3 indexed citations
4.
Micheli, Alessio, et al.. (2024). XAI and Bias of Deep Graph Networks. CINECA IRIS Institutial research information system (University of Pisa). 41–46.
5.
Bacciu, Davide, Federico Errica, Alessio Micheli, et al.. (2023). Graph Representation Learning. CINECA IRIS Institutial research information system (University of Pisa). 1–10. 1 indexed citations
6.
Micheli, Alessio, et al.. (2023). Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs. Bioinformatics. 39(11). 2 indexed citations
7.
Bacciu, Davide, et al.. (2023). Deep Graph Networks for Drug Repurposing With Multi-Protein Targets. IEEE Transactions on Emerging Topics in Computing. 12(1). 177–189. 5 indexed citations
8.
Bacciu, Davide & Marco Podda. (2021). Graphgen-redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation. CINECA IRIS Institutial research information system (University of Pisa). 1–8. 1 indexed citations
9.
Podda, Marco, Davide Bacciu, & Alessio Micheli. (2020). A Deep Generative Model for Fragment-Based Molecule Generation.. CINECA IRIS Institutial research information system (University of Pisa). 2240–2250. 4 indexed citations
10.
Bacciu, Davide, Federico Errica, Alessio Micheli, & Marco Podda. (2020). A gentle introduction to deep learning for graphs. Neural Networks. 129. 203–221. 190 indexed citations
11.
Bacciu, Davide, Alessio Micheli, & Marco Podda. (2020). Edge-based sequential graph generation with recurrent neural networks. Neurocomputing. 416. 177–189. 15 indexed citations
12.
Bove, Pasquale, Alessio Micheli, Paolo Milazzo, & Marco Podda. (2020). Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks. 32–43. 3 indexed citations
13.
Bove, Pasquale, Alessio Micheli, Paolo Milazzo, & Marco Podda. (2020). Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks. CINECA IRIS Institutial research information system (University of Pisa). 32–43. 2 indexed citations
14.
Bacciu, Davide, Alessio Micheli, & Marco Podda. (2019). Graph generation by sequential edge prediction.. The European Symposium on Artificial Neural Networks. 95–100. 4 indexed citations
15.
Errica, Federico, Marco Podda, Davide Bacciu, & Alessio Micheli. (2019). A Fair Comparison of Graph Neural Networks for Graph Classification. arXiv (Cornell University). 49 indexed citations
16.
Podda, Marco, et al.. (2018). A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor. Scientific Reports. 8(1). 13743–13743. 61 indexed citations
17.
Podda, Marco, C. W. Weber, Maret G. Traber, & Lester Packer. (1996). Simultaneous determination of tissue tocopherols, tocotrienols, ubiquinols, and ubiquinones. Journal of Lipid Research. 37(4). 893–901. 293 indexed citations
18.
Podda, Marco, Hans Tritschler, Heinz Ulrich, & Lester Packer. (1994). α-Lipoic Acid Supplementation Prevents Symptoms of Vitamin E Deficiency. Biochemical and Biophysical Research Communications. 204(1). 98–104. 82 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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