Pradeep Muthukrishnan

671 total citations
11 papers, 349 citations indexed

About

Pradeep Muthukrishnan is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and General Health Professions. According to data from OpenAlex, Pradeep Muthukrishnan has authored 11 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Statistical and Nonlinear Physics and 2 papers in General Health Professions. Recurrent topics in Pradeep Muthukrishnan's work include Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers) and Complex Network Analysis Techniques (3 papers). Pradeep Muthukrishnan is often cited by papers focused on Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers) and Complex Network Analysis Techniques (3 papers). Pradeep Muthukrishnan collaborates with scholars based in United States and Uruguay. Pradeep Muthukrishnan's co-authors include Dragomir Radev, Vahed Qazvinian, Amjad Abu-Jbara, Murillo Campello, Bryan R. Gibson, Qiaozhu Mei and Daniel Ferrés and has published in prestigious journals such as Journal of Financial and Quantitative Analysis, Language Resources and Evaluation and Journal of the Association for Information Science and Technology.

In The Last Decade

Pradeep Muthukrishnan

10 papers receiving 318 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pradeep Muthukrishnan United States	7	260	75	44	42	32	11	349
Or Biran United States	10	352 1.4×	47 0.6×	18 0.4×	27 0.6×	10 0.3×	18	416
Kyo Kageura Japan	9	447 1.7×	92 1.2×	92 2.1×	24 0.6×	13 0.4×	92	605
Patrick Glenisson Belgium	8	118 0.5×	76 1.0×	128 2.9×	32 0.8×	17 0.5×	10	310
Kapil Thadani United States	12	299 1.1×	42 0.6×	17 0.4×	18 0.4×	7 0.2×	27	355
Zhunchen Luo China	9	199 0.8×	74 1.0×	34 0.8×	52 1.2×	13 0.4×	23	290
Drahomíra Herrmannová United States	8	104 0.4×	45 0.6×	22 0.5×	15 0.4×	5 0.2×	26	264
Ida Mele Italy	10	132 0.5×	85 1.1×	6 0.1×	34 0.8×	9 0.3×	30	252
Yoan Gutiérrez Spain	10	246 0.9×	59 0.8×	33 0.8×	21 0.5×	9 0.3×	66	309
Ana Lelescu United States	7	69 0.3×	38 0.5×	48 1.1×	12 0.3×	8 0.3×	12	188
Michael Wick United States	13	427 1.6×	98 1.3×	52 1.2×	12 0.3×	6 0.2×	29	506

Countries citing papers authored by Pradeep Muthukrishnan

Since Specialization

Citations

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

Fields of papers citing papers by Pradeep Muthukrishnan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pradeep Muthukrishnan

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Campello, Murillo, et al.. (2023). Corporate Hiring Under COVID-19: Financial Constraints and the Nature of New Jobs. Journal of Financial and Quantitative Analysis. 59(4). 1541–1585. 17 indexed citations

2.

Ferrés, Daniel, et al.. (2023). Anti-Poaching Agreements, Innovation, and Corporate Value: Evidence from the Technology Industry. SSRN Electronic Journal.

3.

Campello, Murillo, et al.. (2020). Corporate Hiring under COVID-19: Labor Market Concentration, Downskilling, and Income Inequality. SSRN Electronic Journal. 31 indexed citations

4.

Radev, Dragomir, et al.. (2015). A bibliometric and network analysis of the field of computational linguistics. Journal of the Association for Information Science and Technology. 67(3). 683–706. 41 indexed citations

5.

Radev, Dragomir, Pradeep Muthukrishnan, Vahed Qazvinian, & Amjad Abu-Jbara. (2013). The ACL anthology network corpus. Language Resources and Evaluation. 47(4). 919–944. 145 indexed citations

6.

Muthukrishnan, Pradeep, Dragomir Radev, & Qiaozhu Mei. (2011). Simultaneous similarity learning and feature-weight learning for document clustering. 42–50. 8 indexed citations

7.

Muthukrishnan, Pradeep, Dragomir Radev, & Qiaozhu Mei. (2010). Edge Weight Regularization over Multiple Graphs for Similarity Learning. 2. 374–383. 17 indexed citations

8.

Radev, Dragomir, Pradeep Muthukrishnan, & Vahed Qazvinian. (2009). The ACL Anthology Network corpus. 54–54. 84 indexed citations

9.

Radev, Dragomir, Pradeep Muthukrishnan, & Vahed Qazvinian. (2009). The ACL Anthology Network. 54–61. 4 indexed citations

10.

Muthukrishnan, Pradeep & Dragomir Radev. (2008). Algorithms for Information Retrieval and Natural Language Processing tasks. 1 indexed citations

11.

Muthukrishnan, Pradeep, et al.. (2008). Detecting multiple facets of an event using graph-based unsupervised methods. 1. 609–616. 1 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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