Prithviraj Sen

5.2k citations
37 papers · 2.9k · 1 hit paper · h-index 18

Impact in

Papers in

Prithviraj Sen

37 papers receiving 2.8k citations

Prithviraj Sen's Hit Papers

Collective Classification in Network Data 2008 · 1.8k citations
1.8k0+6+12Years since publication50010001.5k

Peers

Prithviraj Sen
Comparison fields: 5 of 94
  • Artificial Intelligence 2.3k
  • Statistical and Nonlinear Physics 745
  • Signal Processing 341
  • Computer Vision and Pattern Recognition 543
  • Computational Mathematics 15
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Citations per field
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Citations per year

Countries citing papers authored by Prithviraj Sen

Since Specialization
Citations

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

Fields of papers citing papers by Prithviraj Sen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Collective Classification in Network Data
Hit paper breakdown →
20081830
2 2007161
3 2016115
4 2020103
5 200771
6 200962
7 201462
8 201157
9 200849
10 201747
11 201839
12 201030
13
SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs.
201428
14 201225
15
SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning.
201722
16 201322
17 202219
18 201117
19 201917
20 202117

About Prithviraj Sen

Prithviraj Sen is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Information Systems and Signal Processing, having authored 37 papers that have together received 2.9k indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (8 papers), Data Quality and Management (8 papers), Bayesian Modeling and Causal Inference (7 papers), Data Management and Algorithms (6 papers), Explainable Artificial Intelligence (XAI) (6 papers), Advanced Database Systems and Queries (6 papers) and Advanced Graph Neural Networks (5 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Statistical and Nonlinear Physics (745 citations), Signal Processing (341 citations), Computer Vision and Pattern Recognition (543 citations) and Computational Mathematics (15 citations). Prithviraj Sen has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Lise Getoor, Brian Gallagher, Tina Eliassi‐Rad, Galileo Namata, Mustafa Bilgic, Amol Deshpande, Kun Qian, Berthold Reinwald, Matthias Böehm and Shirish Tatikonda. Their work appears in journals such as Proceedings of the VLDB Endowment, Data Mining and Knowledge Discovery, AI Magazine, The VLDB Journal and Proceedings of the AAAI Conference on Artificial Intelligence.

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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