Pierre-André Savalle

412 total citations
2 papers, 78 citations indexed

About

Pierre-André Savalle is a scholar working on Computer Networks and Communications, Statistical and Nonlinear Physics and Computational Mechanics. According to data from OpenAlex, Pierre-André Savalle has authored 2 papers receiving a total of 78 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Computer Networks and Communications, 1 paper in Statistical and Nonlinear Physics and 1 paper in Computational Mechanics. Recurrent topics in Pierre-André Savalle's work include Sparse and Compressive Sensing Techniques (1 paper), Complex Network Analysis Techniques (1 paper) and Advanced Graph Neural Networks (1 paper). Pierre-André Savalle is often cited by papers focused on Sparse and Compressive Sensing Techniques (1 paper), Complex Network Analysis Techniques (1 paper) and Advanced Graph Neural Networks (1 paper). Pierre-André Savalle collaborates with scholars based in France, Netherlands and Spain. Pierre-André Savalle's co-authors include Émile Richard, Nicolas Vayatis, Rui Castro and Gábor Lugosi and has published in prestigious journals such as IEEE Transactions on Information Theory and arXiv (Cornell University).

In The Last Decade

Pierre-André Savalle

2 papers receiving 78 citations

Peers

Pierre-André Savalle

CM

AI

CVPR

SP

Yuansi Chen United States

Xinyang Yi United States

Ewa Skubalska-Rafajłowicz Poland

Ross Boczar United States

Botong Wang United States

Nicolas Flammarion United States

Khanh Do Ba United States

Nicolas Keriven France

Niklas Koep Germany

Jason M. Klusowski United States

Yuansi Chen United States

Pierre-André Savalle

17 ×0.4

CM

23 ×0.9

AI

35 ×2.1

CVPR

14 ×1.0

SP

23 ×1.8

SP

Citations per year, relative to Pierre-André Savalle Pierre-André Savalle (= 1×) peers Yuansi Chen

Countries citing papers authored by Pierre-André Savalle

Since Specialization

Citations

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

Fields of papers citing papers by Pierre-André Savalle

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pierre-André Savalle. 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 Pierre-André Savalle. The network helps show where Pierre-André Savalle may publish in the future.

Co-authorship network of co-authors of Pierre-André Savalle

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

All Works

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2 of 2 papers shown

1.

Castro, Rui, Gábor Lugosi, & Pierre-André Savalle. (2014). Detection of Correlations With Adaptive Sensing. IEEE Transactions on Information Theory. 60(12). 7913–7927. 5 indexed citations

2.

Richard, Émile, Pierre-André Savalle, & Nicolas Vayatis. (2012). Estimation of Simultaneously Sparse and Low Rank Matrices. arXiv (Cornell University). 51–58. 73 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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