Pedro Contreras

2.5k total citations · 1 hit paper
33 papers, 1.5k citations indexed

About

Pedro Contreras is a scholar working on Condensed Matter Physics, Artificial Intelligence and Geophysics. According to data from OpenAlex, Pedro Contreras has authored 33 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Condensed Matter Physics, 7 papers in Artificial Intelligence and 7 papers in Geophysics. Recurrent topics in Pedro Contreras's work include Physics of Superconductivity and Magnetism (9 papers), Advanced Condensed Matter Physics (8 papers) and Data Management and Algorithms (5 papers). Pedro Contreras is often cited by papers focused on Physics of Superconductivity and Magnetism (9 papers), Advanced Condensed Matter Physics (8 papers) and Data Management and Algorithms (5 papers). Pedro Contreras collaborates with scholars based in Venezuela, United Kingdom and United States. Pedro Contreras's co-authors include Fionn Murtagh, Vladimir Grechka, Ilya Tsvankin, M. B. Walker, G. M. DOWNS, Michael Walker, K. V. Samokhin, Pilar Arroyo, Danielle Costa Morais and Fionn Murtagh and has published in prestigious journals such as SHILAP Revista de lepidopterología, Physical review. B, Condensed matter and Physical Review B.

In The Last Decade

Pedro Contreras

27 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

Algorithms for hierarchical clustering: an overview

2011 1.1k citations Fionn Murtagh, Pedro Contreras Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery profile →

Peers

Pedro Contreras

AI

CVPR

MB

SP

IS

Yanxia Zhang China

Gilles Louppe Belgium

Ray D. Nelson United States

Wendy L. Martinez United States

Valeria V. Krzhizhanovskaya Netherlands

Javier Muguerza Spain

Dana Rezazadegan Australia

Wai Wan Tsang Hong Kong

Yadolah Dodge Switzerland

D.L. Vilariño Spain

Yanxia Zhang China

Pedro Contreras

235 ×0.6

AI

193 ×1.1

CVPR

80 ×0.6

MB

84 ×0.7

SP

84 ×0.8

IS

Citations per year, relative to Pedro Contreras Pedro Contreras (= 1×) peers Yanxia Zhang

Countries citing papers authored by Pedro Contreras

Since Specialization

Citations

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

Fields of papers citing papers by Pedro Contreras

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pedro Contreras

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

All Works

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20 of 20 papers shown

1.

Contreras, Pedro, et al.. (2024). How AI can Help Learners to Develop Conceptual Knowledge in Digital Learning Environments. 1–6.

2.

Ashutosh, Ashutosh, et al.. (2023). Computational insight into structural, electronic and thermal properties of novel two-dimensional NiXO (X = Cl, Br) monolayers: Ab initio perspective. Materials Today Proceedings. 5 indexed citations

3.

Contreras, Pedro, et al.. (2023). A Tale of the Scattering Lifetime and the Mean Free Path. SSRN Electronic Journal.

4.

Grechka, Vladimir, Ilya Tsvankin, & Pedro Contreras. (2023). Inversion of walkaway VSP data in the presence of lateral velocity heterogeneity. Energy Geoscience. 5(2). 100255–100255. 1 indexed citations

5.

Contreras, Pedro, et al.. (2022). The effect of nonmagnetic disorder in the superconducting energy gap of strontium ruthenate. Physica B Condensed Matter. 646. 414330–414330. 1 indexed citations

6.

Contreras, Pedro, et al.. (2022). Tight-Binding Superconducting Phases in the Unconventional Compounds Strontium-Substituted Lanthanum Cuprate and Strontium Ruthenate. American Journal of Modern Physics. 11(2). 32–32. 1 indexed citations

7.

Contreras, Pedro, et al.. (2022). Dressed behavior of the quasiparticles lifetime in the unitary limit of two unconventional superconductors. Low Temperature Physics. 48(3). 187–192. 4 indexed citations

8.

Contreras, Pedro, et al.. (2021). A multi-criteria and stochastic robustness analysis approach to compare nations sustainability. Socio-Economic Planning Sciences. 80. 101159–101159. 5 indexed citations

9.

Murtagh, Fionn & Pedro Contreras. (2017). Algorithms for hierarchical clustering: an overview,II. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 7(6). 159 indexed citations

10.

Rincón, Luís, Rafael Almeida, Pedro Contreras, & F. Javier Torres. (2015). The information content of the conditional pair probability. Chemical Physics Letters. 635. 116–119. 7 indexed citations

11.

Contreras, Pedro & Fionn Murtagh. (2012). Linear time Baire hierarchical clustering for enterprise information retrieval. DMU Open Research Archive (De Montfort University). 6. 363–380.

12.

Murtagh, Fionn & Pedro Contreras. (2012). Fast, linear time, m-adic hierarchical clustering for search and retrieval using the Baire metric, with linkages to generalized ultrametrics, hashing, formal concept analysis, and precision of data measurement. P-Adic Numbers Ultrametric Analysis and Applications. 4(1). 46–56. 8 indexed citations

13.

Contreras, Pedro & Fionn Murtagh. (2005). AstroGrid as an e-Learning Environment. Electronic workshops in computing. 2 indexed citations

14.

Contreras, Pedro, Michael Walker, & K. V. Samokhin. (2004). Determining the superconducting gap structure inSr2RuO4from sound attenuation studies belowTc. Physical Review B. 70(18). 11 indexed citations

15.

Contreras, Pedro & Michael Walker. (2003). Theory of Elastic Properties of Sr_2RuO 4 at the Superconducting Transition Temperature. APS March Meeting Abstracts. 2003. 4 indexed citations

16.

Contreras, Pedro, et al.. (2001). User interest correlation in log data. ePrints Soton (University of Southampton). 1 indexed citations

17.

Grechka, Vladimir, Pedro Contreras, & Ilya Tsvankin. (2000). Inversion of normal moveout for monoclinic media¹. Geophysical Prospecting. 48(3). 577–602. 36 indexed citations

18.

Contreras, Pedro, Vladimir Grechka, & Ilya Tsvankin. (1999). Moveout inversion of P -wave data for horizontal transverse isotropy. Geophysics. 64(4). 1219–1229. 24 indexed citations

19.

Contreras, Pedro, et al.. (1997). Ellipsoidal Approximation of Velocities in Orthorhombic Media. 1 indexed citations

20.

Kaganov, M. I., et al.. (1994). Theory of the anomalous skin effect in metals with complicated Fermi surfaces. Journal of Experimental and Theoretical Physics. 79(6). 985–993. 3 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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