Joan Bruna

21.9k citations
49 papers · 3.8k · 2 hit papers · h-index 17

Impact in

Papers in

Joan Bruna

47 papers receiving 3.7k citations

Joan Bruna's Hit Papers

Geometric Deep Learning: Going beyond Euclidean data 2017 · 1.9k citations
1.9k0+4+8Years since publication50010001.5k

Peers

Joan Bruna
Comparison fields: 5 of 168
  • Computer Vision and Pattern Recognition 1.4k
  • Computer Graphics and Computer-Aided Design 182
  • Artificial Intelligence 1.4k
  • Statistical and Nonlinear Physics 395
  • Computational Mathematics 17
Replace Zachary DeVito with:
Zachary DeVito United States
Arthur Szlam United States
Edward Z. Yang United States
Marco Cuturi France
Alban Desmaison United Kingdom
Adam Paszke United States
豊 松尾
Yaakov Weiss Israel
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Joan Bruna relative to Zachary DeVito United States Zachary DeVito's profile →
Citations per field
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Citations per year

Countries citing papers authored by Joan Bruna

Since Specialization
Citations

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

Fields of papers citing papers by Joan Bruna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Joan Bruna, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Joan Bruna Line = papers co-authored together Joan Bruna links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Geometric Deep Learning: Going beyond Euclidean data
Hit paper breakdown →
20171950
2
Invariant Scattering Convolution Networks
Hit paper breakdown →
2013951
3 2020129
4 202089
5 201177
6 201664
7 201563
8
Supervised community detection with line graph neural networks
201952
9 201851
10 201539
11 201832
12 202232
13 201832
14
Neural Message Passing for Jet Physics
201725
15
Community Detection with Graph Neural Networks
201722
16 202321
17 202116
18 201916
19
Surface Networks
201815
20 202112

About Joan Bruna

Joan Bruna is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics and Computational Theory and Mathematics, having authored 49 papers that have together received 3.8k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (9 papers), Advanced Graph Neural Networks (7 papers), Complex Network Analysis Techniques (6 papers), Topological and Geometric Data Analysis (5 papers), 3D Shape Modeling and Analysis (5 papers), Markov Chains and Monte Carlo Methods (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Stochastic Gradient Optimization Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Computer Graphics and Computer-Aided Design (182 citations), Artificial Intelligence (1.4k citations), Statistical and Nonlinear Physics (395 citations) and Computational Mathematics (17 citations). Joan Bruna has collaborated with scholars based in United States, France and Switzerland. Frequent co-authors include Stéphane Mallat, Yann LeCun, Arthur Szlam, Michael M. Bronstein, Pierre Vandergheynst, Alejandro Ribeiro, Fernando Gama, Brice Ménard, Yuan-Sen Ting and Sihao Cheng. Their work appears in journals such as Journal of Computational Physics, Monthly Notices of the Royal Astronomical Society, The Annals of Statistics, Journal of Machine Learning Research and IEEE Signal Processing Magazine.

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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