Arvind Rajan

545 citations
30 papers · 462 · h-index 12

Impact in

Papers in

Arvind Rajan

30 papers receiving 446 citations

Peers

Arvind Rajan
Comparison fields: 5 of 70
  • Management Information Systems 167
  • Statistics, Probability and Uncertainty 98
  • Marketing 95
  • Industrial and Manufacturing Engineering 79
  • Strategy and Management 91
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Tobias Meyer Germany
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Citations per field
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Citations per year

Countries citing papers authored by Arvind Rajan

Since Specialization
Citations

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

Fields of papers citing papers by Arvind Rajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Arvind Rajan, 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 Arvind Rajan Line = papers co-authored together Arvind Rajan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1992181
2 201832
3 201429
4 199425
5 201722
6 201521
7 201520
8 201717
9 201713
10 201713
11 201912
12 202011
13 198910
14 201710
15 20167
16
Algorithmic applications of connectivity and related topics in matroid theory
19877
17 20145
18 20194
19 19904
20 20174

About Arvind Rajan

Arvind Rajan is a scholar working on Statistics, Probability and Uncertainty, Computer Networks and Communications, Biomedical Engineering, Computational Theory and Mathematics and Civil and Structural Engineering, having authored 30 papers that have together received 462 indexed citations. Recurring topics across this work include Scientific Measurement and Uncertainty Evaluation (11 papers), Probabilistic and Robust Engineering Design (11 papers), Advanced Sensor Technologies Research (8 papers), Sensor Technology and Measurement Systems (6 papers), Advanced Multi-Objective Optimization Algorithms (5 papers), Structural Health Monitoring Techniques (4 papers), Optimal Experimental Design Methods (3 papers) and Advanced Battery Technologies Research (2 papers). The work is most often cited by research in Management Information Systems (167 citations), Statistics, Probability and Uncertainty (98 citations), Marketing (95 citations), Industrial and Manufacturing Engineering (79 citations) and Strategy and Management (91 citations). Arvind Rajan has collaborated with scholars based in Malaysia, New Zealand and Australia. Frequent co-authors include Rakesh Rakesh, Richard Steinberg, Ye Chow Kuang, Melanie Po‐Leen Ooi, Serge Demidenko, John G. Klincewicz, Akhil Garg, V. Vijayaraghavan, Sarbajit Paul and Junghwan Chang. Their work appears in journals such as Measurement, IEEE Access, IEEE Transactions on Instrumentation and Measurement, The Journal of Engineering and Naval Research Logistics (NRL).

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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