Ganesh Krishnan

1.1k total citations · 1 hit paper
11 papers, 650 citations indexed

About

Ganesh Krishnan is a scholar working on Management Science and Operations Research, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Ganesh Krishnan has authored 11 papers receiving a total of 650 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Management Science and Operations Research, 5 papers in Information Systems and 3 papers in Computer Networks and Communications. Recurrent topics in Ganesh Krishnan's work include Data Quality and Management (7 papers), Web Data Mining and Analysis (5 papers) and Advanced Database Systems and Queries (3 papers). Ganesh Krishnan is often cited by papers focused on Data Quality and Management (7 papers), Web Data Mining and Analysis (5 papers) and Advanced Database Systems and Queries (3 papers). Ganesh Krishnan collaborates with scholars based in United States. Ganesh Krishnan's co-authors include AnHai Doan, Esteban Arcaute, Youngchoon Park, Sidharth Mudgal, Han Li, Theodoros Rekatsinas, Sanjib Das, Shishir Prasad, Haojun Zhang and Han Li and has published in prestigious journals such as Soft Matter, Proceedings of the VLDB Endowment and ACM SIGMOD Record.

In The Last Decade

Ganesh Krishnan

11 papers receiving 594 citations

Hit Papers

Deep Learning for Entity Matching 2018 2026 2020 2023 2018 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ganesh Krishnan United States 8 464 460 167 75 50 11 650
Dustin Lange Germany 10 188 0.4× 269 0.6× 84 0.5× 43 0.6× 18 446
Yunyao Li United States 12 121 0.3× 410 0.9× 179 1.1× 71 0.9× 43 642
Varish Mulwad United States 9 115 0.2× 198 0.4× 220 1.3× 123 1.6× 21 359
Yu Hong China 12 46 0.1× 468 1.0× 106 0.6× 16 0.2× 2 0.0× 55 631
Mesut Kaya Türkiye 10 37 0.1× 128 0.3× 117 0.7× 9 0.1× 3 0.1× 32 342
Peter Bloodsworth United Kingdom 11 39 0.1× 98 0.2× 119 0.7× 98 1.3× 24 277
Huizhi Liang United Kingdom 12 75 0.2× 262 0.6× 264 1.6× 65 0.9× 57 422
Caihua Shan China 6 74 0.2× 227 0.5× 47 0.3× 23 0.3× 11 373
Νικόλαος Κωνσταντίνου Greece 12 73 0.2× 159 0.3× 151 0.9× 113 1.5× 39 373
Sha Yuan China 9 10 0.0× 227 0.5× 107 0.6× 48 0.6× 5 0.1× 31 391

Countries citing papers authored by Ganesh Krishnan

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Ganesh Krishnan

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

All Works

11 of 11 papers shown
1.
Li, Han, Pradap Konda, AnHai Doan, et al.. (2018). MatchCatcher: A Debugger for Blocking in Entity Matching. Movebank. 9 indexed citations
2.
Krishnan, Ganesh & Heike Hofmann. (2018). Adapting the Chumbley Score to Match Striae on Land Engraved Areas (LEAs) of Bullets,. Journal of Forensic Sciences. 64(3). 728–740. 1 indexed citations
3.
Konda, Pradap, Sanjib Das, Paul Suganthan, et al.. (2018). Technical Perspective:. ACM SIGMOD Record. 47(1). 33–40. 4 indexed citations
4.
Mudgal, Sidharth, Han Li, Theodoros Rekatsinas, et al.. (2018). Deep Learning for Entity Matching. 19–34. 282 indexed citations breakdown →
5.
Perez‐Meza, David, et al.. (2017). Hair follicle growth by stromal vascular fraction-enhanced adipose transplantation in baldness. PubMed. Volume 10. 1–10. 62 indexed citations
6.
Das, Sanjib, Paul Suganthan, AnHai Doan, et al.. (2017). Falcon. 1431–1446. 63 indexed citations
7.
Konda, Pradap, Sanjib Das, AnHai Doan, et al.. (2016). Magellan. Proceedings of the VLDB Endowment. 9(12). 1197–1208. 153 indexed citations
8.
Konda, Pradap, Sanjib Das, AnHai Doan, et al.. (2016). Magellan. Proceedings of the VLDB Endowment. 9(13). 1581–1584. 41 indexed citations
9.
Suganthan, Paul, Haojun Zhang, Shishir Prasad, et al.. (2015). Why Big Data Industrial Systems Need Rules and What We Can Do About It. 265–276. 17 indexed citations
10.
Krishnan, Ganesh, et al.. (2013). Correlating fullerene diffusion with the polythiophene morphology: molecular dynamics simulations. Soft Matter. 9(42). 10048–10048. 12 indexed citations
11.
Enberg, Robert, et al.. (2012). Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts. BMC Medical Informatics and Decision Making. 12(1). 16–16. 6 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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