Pradeep Muthukrishnan

680 citations
11 papers · 354 · h-index 7

Impact in

Papers in

    • Natural Language Processing Techniques 5
    • Topic Modeling 5
    • Semantic Web and Ontologies 3
    • Text and Document Classification Technologies 3
    • Advanced Graph Neural Networks 2
    • Advanced Text Analysis Techniques 2
    • Complex Network Analysis Techniques 3

Pradeep Muthukrishnan

10 papers receiving 325 citations

Peers

Pradeep Muthukrishnan
Comparison fields: 5 of 64
  • Artificial Intelligence 262
  • Statistics, Probability and Uncertainty 26
  • Statistical and Nonlinear Physics 42
  • Information Systems 75
  • General Social Sciences 10
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Citations per year

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-authors

The 7 scholars most cited alongside Pradeep Muthukrishnan, 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 Pradeep Muthukrishnan Line = papers co-authored together Pradeep Muthukrishnan links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 2013146
2 200985
3 201541
4 202031
5 202320
6 201017
7
Simultaneous similarity learning and feature-weight learning for document clustering
20118
8
The ACL Anthology Network
20094
9
Algorithms for Information Retrieval and Natural Language Processing tasks
20081
10 20081
11 20230

About Pradeep Muthukrishnan

Pradeep Muthukrishnan is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, General Health Professions, Economics and Econometrics and Molecular Biology, having authored 11 papers that have together received 354 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (5 papers), Complex Network Analysis Techniques (3 papers), Semantic Web and Ontologies (3 papers), Text and Document Classification Technologies (3 papers), Labor market dynamics and wage inequality (2 papers), Advanced Graph Neural Networks (2 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (262 citations), Statistics, Probability and Uncertainty (26 citations), Statistical and Nonlinear Physics (42 citations), Information Systems (75 citations) and General Social Sciences (10 citations). Pradeep Muthukrishnan has collaborated with scholars based in United States and Uruguay. Frequent co-authors include Dragomir Radev, Vahed Qazvinian, Amjad Abu-Jbara, Murillo Campello, Bryan R. Gibson, Qiaozhu Mei and Daniel Ferrés. Their work appears in journals such as Language Resources and Evaluation, Journal of Financial and Quantitative Analysis, Journal of the Association for Information Science and Technology and SSRN Electronic Journal.

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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