Petar Veličković

14.4k total citations · 2 hit papers
28 papers, 910 citations indexed

About

Petar Veličković is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Petar Veličković has authored 28 papers receiving a total of 910 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 7 papers in Molecular Biology and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Petar Veličković's work include Advanced Graph Neural Networks (6 papers), Sports Performance and Training (4 papers) and vaccines and immunoinformatics approaches (3 papers). Petar Veličković is often cited by papers focused on Advanced Graph Neural Networks (6 papers), Sports Performance and Training (4 papers) and vaccines and immunoinformatics approaches (3 papers). Petar Veličković collaborates with scholars based in United Kingdom, Serbia and Canada. Petar Veličković's co-authors include Píetro Lió, Pietro Sormanni, Michele Vendruscolo, Yoshua Bengio, Edgar Liberis, William L. Hamilton, Charles Blundell, Lars Buesing, R Devon Hjelm and William Fedus and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Petar Veličković

24 papers receiving 864 citations

Hit Papers

Advancing mathematics by guiding human intuition with AI 2021 2026 2022 2024 2021 2023 50 100 150 200
What are hit papers?

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.

  1. Advancing mathematics by guiding human intuition with AI
    2021 243 citations Alex Davies, Petar Veličković et al. Nature profile →
  2. Everything is connected: Graph neural networks
    2023 141 citations Petar Veličković Current Opinion in Structural Biology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Petar Veličković United Kingdom 11 388 218 144 142 86 28 910
Zhiqiang Hu China 11 313 0.8× 141 0.6× 74 0.5× 161 1.1× 131 1.5× 52 785
Alessandro Rinaldo United States 20 565 1.5× 139 0.6× 59 0.4× 108 0.8× 203 2.4× 57 1.5k
Erik Nijkamp United States 10 416 1.1× 178 0.8× 55 0.4× 271 1.9× 37 0.4× 22 1.1k
Issei Sato Japan 19 698 1.8× 120 0.6× 83 0.6× 207 1.5× 30 0.3× 75 1.2k
Garvesh Raskutti United States 19 667 1.7× 160 0.7× 55 0.4× 125 0.9× 69 0.8× 41 1.8k
Michał Walczak Switzerland 11 178 0.5× 254 1.2× 41 0.3× 40 0.3× 81 0.9× 19 816
Bei Wang United States 16 242 0.6× 93 0.4× 77 0.5× 515 3.6× 368 4.3× 110 1.1k
Weixin Xie China 17 526 1.4× 95 0.4× 40 0.3× 201 1.4× 226 2.6× 72 1.5k
Davide Boscaini Italy 7 562 1.4× 402 1.8× 74 0.5× 711 5.0× 221 2.6× 20 1.8k
I. García Spain 20 267 0.7× 135 0.6× 51 0.4× 188 1.3× 263 3.1× 121 1.6k

Countries citing papers authored by Petar Veličković

Since Specialization
Citations

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

Fields of papers citing papers by Petar Veličković

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Petar Veličković

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

All Works

20 of 20 papers shown
1.
2.
Veličković, Petar, et al.. (2025). The Impact of an Acrobatics-Based Curriculum on Motor Fitness in Adolescents. Life. 15(5). 778–778. 1 indexed citations
3.
Rodolà, Emanuele, et al.. (2024). Geometric epitope and paratope prediction. Bioinformatics. 40(7). 1 indexed citations
4.
Veličković, Petar, et al.. (2024). Does the Impact Force Depend on Arm Position and Run-up Speed during Forward Handspring on the Vault in Elite Gymnasts? A Pilot Study. SHILAP Revista de lepidopterología. 15(Vol.15, no. 2). 685–685.
5.
Veličković, Petar, et al.. (2023). Technical elements on the rings in men's artistic gymnastics—a systematic review. SHILAP Revista de lepidopterología.
6.
Veličković, Petar, et al.. (2023). Is the handgrip strength influential factor on the competition result in elite male artistic gymnasts?. SHILAP Revista de lepidopterología. 1 indexed citations
7.
Simeone, Osvaldo, et al.. (2023). Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating. Machine Learning Science and Technology. 4(4). 45038–45038. 1 indexed citations
8.
Veličković, Petar. (2023). Everything is connected: Graph neural networks. Current Opinion in Structural Biology. 79. 102538–102538. 141 indexed citations breakdown →
9.
Barbiero, Pietro, et al.. (2022). Algorithmic Concept-Based Explainable Reasoning. Proceedings of the AAAI Conference on Artificial Intelligence. 36(6). 6685–6693. 3 indexed citations
10.
Cappart, Quentin, Didier Chételat, Elias B. Khalil, et al.. (2021). Combinatorial Optimization and Reasoning with Graph Neural Networks. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 105 indexed citations
11.
Hamrick, Jessica B., Arthur Guez, Fabio Viola, et al.. (2021). On the role of planning in model-based deep reinforcement learning. arXiv (Cornell University). 10 indexed citations
12.
Strathmann, Heiko, Mohammadamin Barekatain, Charles D. Blundell, & Petar Veličković. (2021). Persistent Message Passing. arXiv (Cornell University). 1 indexed citations
13.
Davies, Alex, Petar Veličković, Lars Buesing, et al.. (2021). Advancing mathematics by guiding human intuition with AI. Nature. 600(7887). 70–74. 243 indexed citations breakdown →
14.
Cangea, Cătălina, Petar Veličković, & Píetro Lió. (2020). XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification. IRIS Research product catalog (Sapienza University of Rome). 22 indexed citations
15.
Corso, Gabriele, et al.. (2020). Principal Neighbourhood Aggregation for Graph Nets. Neural Information Processing Systems. 33. 13260–13271. 14 indexed citations
16.
Veličković, Petar, et al.. (2020). ChronoMID—Cross-modal neural networks for 3-D temporal medical imaging data. PLoS ONE. 15(2). e0228962–e0228962.
17.
Deac, Andreea, Petar Veličković, & Pietro Sormanni. (2019). Attentive Cross-Modal Paratope Prediction.. Apollo (University of Cambridge). 42 indexed citations
18.
Liberis, Edgar, Petar Veličković, Pietro Sormanni, Michele Vendruscolo, & Píetro Lió. (2018). Parapred: antibody paratope prediction using convolutional and recurrent neural networks. Bioinformatics. 34(17). 2944–2950. 112 indexed citations
19.
Bica, Ioana, et al.. (2018). Multi-omics data integration using cross-modal neural networks.. The European Symposium on Artificial Neural Networks. 5 indexed citations
20.
Veličković, Petar & Píetro Lió. (2015). Molecular multiplex network inference using Gaussian mixture hidden Markov models. Journal of Complex Networks. cnv029–cnv029. 2 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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