Fatemeh Nargesian

1.3k citations
31 papers · 753 · h-index 13

Impact in

Papers in

Fatemeh Nargesian

29 papers receiving 723 citations

Peers

Fatemeh Nargesian
Comparison fields: 5 of 103
  • Management Science and Operations Research 359
  • Information Systems and Management 96
  • Signal Processing 138
  • Artificial Intelligence 397
  • Computer Networks and Communications 212
Replace Sudip Roy with:
Sudip Roy United States
Wolfram Wöß Austria
M. J. Baqer Iraq
Lisa Ehrlinger Austria
Iván López-Arévalo Mexico
Vahida Attar India
Deepak Agarwal India
Luís Cruz Netherlands
Richard A. Ikuesan South Africa
Hong Qing Yu United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Fatemeh Nargesian

Since Specialization
Citations

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

Fields of papers citing papers by Fatemeh Nargesian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017172
2 2019124
3 2018117
4 201689
5 201972
6
Making Open Data Transparent: Data Discovery on Open Data.
201822
7 202019
8 202118
9 202118
10 201118
11 202217
12 201716
13 201314
14 20239
15 20227
16 20233
17 20203
18 20232
19 20112
20 20232

About Fatemeh Nargesian

Fatemeh Nargesian is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Signal Processing and Sociology and Political Science, having authored 31 papers that have together received 753 indexed citations. Recurring topics across this work include Data Quality and Management (16 papers), Data Management and Algorithms (7 papers), Data Stream Mining Techniques (7 papers), Semantic Web and Ontologies (7 papers), Advanced Database Systems and Queries (7 papers), Imbalanced Data Classification Techniques (3 papers), Privacy-Preserving Technologies in Data (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Management Science and Operations Research (359 citations), Information Systems and Management (96 citations), Signal Processing (138 citations), Artificial Intelligence (397 citations) and Computer Networks and Communications (212 citations). Fatemeh Nargesian has collaborated with scholars based in United States, Canada and Iran. Frequent co-authors include Renée J. Miller, Erkang Zhu, Ken Q. Pu, Horst Samulowitz, Udayan Khurana, Deepak S. Turaga, Elias B. Khalil, Patricia C. Arocena, Dong Deng and Abolfazl Asudeh. Their work appears in journals such as Proceedings of the VLDB Endowment, Journal of the Optical Society of America B, Journal of Materials Science, The VLDB Journal and Proceedings of the ACM on Management of Data.

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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