Benoît Gaüzère

689 total citations
14 papers, 141 citations indexed

About

Benoît Gaüzère is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Benoît Gaüzère has authored 14 papers receiving a total of 141 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 5 papers in Computational Theory and Mathematics. Recurrent topics in Benoît Gaüzère's work include Advanced Graph Neural Networks (10 papers), Graph Theory and Algorithms (8 papers) and Computational Drug Discovery Methods (4 papers). Benoît Gaüzère is often cited by papers focused on Advanced Graph Neural Networks (10 papers), Graph Theory and Algorithms (8 papers) and Computational Drug Discovery Methods (4 papers). Benoît Gaüzère collaborates with scholars based in France, Italy and Switzerland. Benoît Gaüzère's co-authors include Luc Brun, Sébastien Adam, Pierre Héroux, Paul Honeiné, Muhammet Balcılar, Guillaume Hoffmann, Laurent Joubert, Vincent Tognetti, Sébastien Bougleux and David B. Blumenthal and has published in prestigious journals such as Expert Systems with Applications, Journal of Computational Chemistry and Pattern Recognition.

In The Last Decade

Benoît Gaüzère

14 papers receiving 139 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Benoît Gaüzère France	7	66	64	42	28	17	14	141
Victor García Satorras Netherlands	6	62 0.9×	88 1.4×	55 1.3×	60 2.1×	7 0.4×	6	222
Gabriele Corso United States	4	17 0.3×	49 0.8×	37 0.9×	33 1.2×	2 0.1×	6	157
Émilie Devijver France	10	20 0.3×	94 1.5×	21 0.5×	72 2.6×	20 1.2×	21	216
Patrick Forré Netherlands	8	45 0.7×	52 0.8×	20 0.5×	12 0.4×	6 0.4×	17	186
Abid Mahboob Pakistan	14	76 1.2×	61 1.0×	244 5.8×	13 0.5×	2 0.1×	44	397
Huacheng Yu United States	10	16 0.2×	68 1.1×	101 2.4×	6 0.2×	7 0.4×	30	167
Emanuele Munarini Italy	9	20 0.3×	37 0.6×	117 2.8×	15 0.5×	3 0.2×	45	301
Huiqin Jiang China	9	13 0.2×	26 0.4×	202 4.8×	13 0.5×	11 0.6×	31	286
Patricia Suriana United States	5	28 0.4×	29 0.5×	33 0.8×	16 0.6×	2 0.1×	7	115
Hanlin Gu Hong Kong	6	19 0.3×	107 1.7×	9 0.2×	14 0.5×	4 0.2×	18	170

Countries citing papers authored by Benoît Gaüzère

Since Specialization

Citations

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

Fields of papers citing papers by Benoît Gaüzère

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benoît Gaüzère

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

All Works

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14 of 14 papers shown

Gaüzère, Benoît, et al.. (2025). Pre-image free graph machine learning with Normalizing Flows. Pattern Recognition Letters. 190. 45–51. 1 indexed citations

Gaüzère, Benoît, et al.. (2024). Graph Neural Networks with maximal independent set-based pooling: Mitigating over-smoothing and over-squashing. Pattern Recognition Letters. 187. 14–20. 1 indexed citations

Brémond, Éric, et al.. (2024). Predicting redox potentials by graph‐based machine learning methods. Journal of Computational Chemistry. 45(28). 2383–2396. 4 indexed citations

Gaüzère, Benoît, et al.. (2023). Detecting dynamic patterns in dynamic graphs using subgraph isomorphism. Pattern Analysis and Applications. 26(3). 1205–1221. 2 indexed citations

Tognetti, Vincent, et al.. (2022). A Study on the Stability of Graph Edit Distance Heuristics. Electronics. 11(20). 3312–3312. 1 indexed citations

Gaüzère, Benoît, et al.. (2021). Graph kernels based on linear patterns: Theoretical and experimental comparisons. Expert Systems with Applications. 189. 116095–116095. 11 indexed citations

Blumenthal, David B., et al.. (2021). Scalable generalized median graph estimation and its manifold use in bioinformatics, clustering, classification, and indexing. Information Systems. 100. 101766–101766. 4 indexed citations

Gaüzère, Benoît, et al.. (2021). graphkit-learn: A Python library for graph kernels based on linear patterns. Pattern Recognition Letters. 143. 113–121. 4 indexed citations

Balcılar, Muhammet, et al.. (2021). Symbols Detection and Classification using Graph Neural Networks. Pattern Recognition Letters. 152. 391–397. 9 indexed citations

10.

Hoffmann, Guillaume, Muhammet Balcılar, Vincent Tognetti, et al.. (2020). Predicting experimental electrophilicities from quantum and topological descriptors: A machine learning approach. Journal of Computational Chemistry. 41(24). 2124–2136. 32 indexed citations

11.

Bougleux, Sébastien, Benoît Gaüzère, David B. Blumenthal, & Luc Brun. (2018). Fast linear sum assignment with error-correction and no cost constraints. Pattern Recognition Letters. 134. 37–45. 8 indexed citations

12.

Gaüzère, Benoît, Sébastien Bougleux, Jean-Yves Ramel, et al.. (2017). Graph edit distance contest: Results and future challenges. Pattern Recognition Letters. 100. 96–103. 14 indexed citations

13.

Gaüzère, Benoît, et al.. (2014). Treelet kernel incorporating cyclic, stereo and inter pattern information in chemoinformatics. Pattern Recognition. 48(2). 356–367. 11 indexed citations

14.

Gaüzère, Benoît, et al.. (2012). Two new graphs kernels in chemoinformatics. Pattern Recognition Letters. 33(15). 2038–2047. 39 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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