V. S. Subrahmanian

18.1k citations
334 papers · 9.6k indexed · 4 hit papers · h-index 46

V. S. Subrahmanian

321 papers receiving 8.8k citations

Hit Papers

REV22152008202620142020250500750

Peers

V. S. Subrahmanian
Comparison fields: 5 of 159
  • Signal Processing 2.1k
  • Artificial Intelligence 5.7k
  • Computer Networks and Communications 3.3k
  • Information Systems 2.1k
  • Computer Vision and Pattern Recognition 1.7k
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Henry Kautz United States
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V. S. Subrahmanian relative to Henry Kautz United States Henry Kautz's profile →
Citations per field
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Citations per year

Countries citing papers authored by V. S. Subrahmanian

Since Specialization
Citations

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

Fields of papers citing papers by V. S. Subrahmanian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by V. S. Subrahmanian. 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 V. S. Subrahmanian. The network helps show where V. S. Subrahmanian may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20240
3 20230
4 20221
5 20221
6 20222
7 202010
8 20207
9 20204
10 201934
11 201422
12 20142
13
Scalable Uncertainty Management - 7th International Conference, SUM 2013
20137
14
Promises kept, promises broken: an axiomatic and quantitative treatment of fulfillment
20084
15
The OASYS 2.0 Opinion Analysis System: A Demo
20072
16
Opinion Analysis in Document Databases.
20061
17
Probabilistically survivable MASs
200310
18
On the complexity of domain-independent planning
199236
19
Dualities between Alternative Semantics for Logic Programming and Nonmonotonic Reasoning (Extended Abstract).
19916
20
Intuitive Semantics for Quantitative Rule Sets.
19882

About V. S. Subrahmanian

V. S. Subrahmanian is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 334 papers that have together received 9.6k indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (98 papers), Semantic Web and Ontologies (76 papers), Advanced Database Systems and Queries (51 papers), Data Management and Algorithms (45 papers), Multi-Agent Systems and Negotiation (30 papers), Logic, programming, and type systems (26 papers), Advanced Malware Detection Techniques (26 papers) and Spam and Phishing Detection (25 papers). The work is most often cited by research in Signal Processing (2.1k citations), Artificial Intelligence (5.7k citations) and Computer Networks and Communications (3.3k citations). V. S. Subrahmanian has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include Octavian Udrea, Sushil Jajodia, Rama Chellappa, Pavan Turaga, Pierangela Samarati, Ashish Gupta, Inderpal Singh Mumick, Raymond T. Ng, Srijan Kumar and Christos Faloutsos. Their work appears in journals such as Science, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.

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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