Ganesh Krishnan
Impact in
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- Data Quality and Management
- Artificial Intelligence top 5%
- Topic Modeling
- Privacy-Preserving Technologies in Data
- Semantic Web and Ontologies
- Advanced Graph Neural Networks
Papers in
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- Data Quality and Management 7
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- Web Data Mining and Analysis 5
- Co-authors
- AnHai Doan (6 shared papers)Esteban Arcaute (3 shared papers)Youngchoon Park (3 shared papers)Sidharth Mudgal (1 shared paper)Han Li (1 shared paper)Theodoros Rekatsinas (1 shared paper)Sanjib Das (4 shared papers)Shishir Prasad (4 shared papers)
- Journals
- Proceedings of the VLDB Endowment (2 papers)Soft Matter (1 paper)BMC Medical Informatics and Decision Making (1 paper)Journal of Forensic Sciences (1 paper)ACM SIGMOD Record (1 paper)
- Partner nations
- United States
In The Last Decade
Ganesh Krishnan
11 papers receiving 594 citations
Ganesh Krishnan's Hit Papers
Peers
Comparison fields: 5 of 66
- Management Science and Operations Research 464
- Artificial Intelligence 460
- Urology 50
- Information Systems 167
- Information Systems and Management 34
Countries citing papers authored by Ganesh Krishnan
This map shows the geographic impact of Ganesh Krishnan'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 Ganesh Krishnan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ganesh Krishnan more than expected).
Fields of papers citing papers by Ganesh Krishnan
This network shows the impact of papers produced by Ganesh Krishnan. 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 Ganesh Krishnan. The network helps show where Ganesh Krishnan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ganesh Krishnan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep Learning for Entity Matching Hit paper breakdown → | 2018 | 282 |
| 2 | 2016 | 153 | |
| 3 | 2017 | 63 | |
| 4 | 2017 | 62 | |
| 5 | 2016 | 41 | |
| 6 | 2015 | 17 | |
| 7 | 2013 | 12 | |
| 8 | 2018 | 9 | |
| 9 | 2012 | 6 | |
| 10 | 2018 | 4 | |
| 11 | 2018 | 1 |
About Ganesh Krishnan
Ganesh Krishnan is a scholar working on Management Science and Operations Research, Information Systems, Computer Networks and Communications, Artificial Intelligence and Organic Chemistry, having authored 11 papers that have together received 650 indexed citations. Recurring topics across this work include Data Quality and Management (7 papers), Web Data Mining and Analysis (5 papers), Advanced Database Systems and Queries (3 papers), Topic Modeling (2 papers), Business Process Modeling and Analysis (1 paper), Dermatologic Treatments and Research (1 paper), Forensic Fingerprint Detection Methods (1 paper) and Forensic and Genetic Research (1 paper). The work is most often cited by research in Management Science and Operations Research (464 citations), Artificial Intelligence (460 citations), Urology (50 citations), Information Systems (167 citations) and Information Systems and Management (34 citations). Ganesh Krishnan has collaborated with scholars based in United States. Frequent co-authors include AnHai Doan, Esteban Arcaute, Youngchoon Park, Sidharth Mudgal, Han Li, Theodoros Rekatsinas, Sanjib Das, Shishir Prasad, Haojun Zhang and Han Li. Their work appears in journals such as Proceedings of the VLDB Endowment, Soft Matter, BMC Medical Informatics and Decision Making, Journal of Forensic Sciences and ACM SIGMOD Record.
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.