Mayank Kejriwal
Impact in
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- Data Quality and Management
- Artificial Intelligence top 5%
- Topic Modeling
- Advanced Graph Neural Networks
- Semantic Web and Ontologies
- Sentiment Analysis and Opinion Mining
- Natural Language Processing Techniques
Papers in
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- Topic Modeling 20
- Semantic Web and Ontologies 17
- Natural Language Processing Techniques 10
- Advanced Graph Neural Networks 7
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- Web Data Mining and Analysis 14
- Co-authors
- Daniel P. Miranker (5 shared papers)Pedro Szekely (10 shared papers)Ke Shen (13 shared papers)Peilin Zhou (2 shared papers)Deborah L. McGuinness (6 shared papers)Vanessa López (1 shared paper)Juan Sequeda (1 shared paper)Rahul Kapoor (1 shared paper)
- Journals
- IEEE Intelligent Systems (3 papers)PLoS ONE (3 papers)Data in Brief (3 papers)Applied Network Science (3 papers)Future Internet (2 papers)
- Partner nations
- United StatesItalyFrance
In The Last Decade
Mayank Kejriwal
76 papers receiving 536 citations
Peers
Comparison fields: 5 of 83
- Management Science and Operations Research 155
- Artificial Intelligence 325
- Health Informatics 12
- Information Systems 125
- Communication 23
Countries citing papers authored by Mayank Kejriwal
This map shows the geographic impact of Mayank Kejriwal'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 Mayank Kejriwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mayank Kejriwal more than expected).
Fields of papers citing papers by Mayank Kejriwal
This network shows the impact of papers produced by Mayank Kejriwal. 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 Mayank Kejriwal. The network helps show where Mayank Kejriwal may publish in the future.
Co-authors
The 25 scholars most cited alongside Mayank Kejriwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 85 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 69 | |
| 2 | 2021 | 36 | |
| 3 | 2022 | 33 | |
| 4 | 2013 | 32 | |
| 5 | 2017 | 27 | |
| 6 | 2015 | 21 | |
| 7 | 2020 | 20 | |
| 8 | 2019 | 17 | |
| 9 | 2022 | 15 | |
| 10 | 2020 | 14 | |
| 11 | 2022 | 13 | |
| 12 | 2018 | 13 | |
| 13 | 2025 | 11 | |
| 14 | 2018 | 11 | |
| 15 | 2019 | 9 | |
| 16 | A two-step blocking scheme learner for scalable link discovery | 2014 | 9 |
| 17 | 2021 | 9 | |
| 18 | 2024 | 8 | |
| 19 | 2023 | 8 | |
| 20 | 2019 | 7 |
About Mayank Kejriwal
Mayank Kejriwal is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Sociology and Political Science and Statistical and Nonlinear Physics, having authored 85 papers that have together received 562 indexed citations. Recurring topics across this work include Topic Modeling (20 papers), Data Quality and Management (18 papers), Semantic Web and Ontologies (17 papers), Web Data Mining and Analysis (14 papers), Natural Language Processing Techniques (10 papers), Advanced Graph Neural Networks (7 papers), Complex Network Analysis Techniques (7 papers) and Advanced Database Systems and Queries (6 papers). The work is most often cited by research in Management Science and Operations Research (155 citations), Artificial Intelligence (325 citations), Health Informatics (12 citations), Information Systems (125 citations) and Communication (23 citations). Mayank Kejriwal has collaborated with scholars based in United States, Italy and France. Frequent co-authors include Daniel P. Miranker, Pedro Szekely, Ke Shen, Peilin Zhou, Deborah L. McGuinness, Vanessa López, Juan Sequeda, Rahul Kapoor, Craig A. Knoblock and Chien-Chun Ni. Their work appears in journals such as IEEE Intelligent Systems, PLoS ONE, Data in Brief, Applied Network Science and Future Internet.
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.