Mukkai S. Krishnamoorthy

77 papers receiving 1.2k citations

Peers

Mukkai S. Krishnamoorthy
Comparison fields: 5 of 116
  • Computational Theory and Mathematics 372
  • Computer Networks and Communications 343
  • Artificial Intelligence 325
  • Information Systems 254
  • Computer Vision and Pattern Recognition 238
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Countries citing papers authored by Mukkai S. Krishnamoorthy

Since Specialization
Citations

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

Fields of papers citing papers by Mukkai S. Krishnamoorthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mukkai S. Krishnamoorthy

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 7
2
A Metrics Suite for Measuring Software Components.
6
3 1
4
FINDING COMMUNITIES BY CLUSTERING A GRAPH INTO OVERLAPPING SUBGRAPHS
107
5
Hardness Results for Cake Cutting
6
6
Solving Crossword Puzzles via the Google API.
0
7
Semantics of links and document structure discovery
1
8
Describing Structure and Semantics of Graphs Using an RDF Vocabulary
1
9
LOGML - XML Language for Web Usage Mining.
2
10
Clusterability Detection and Initial Seed Selection in Large Data Sets
3
11
WWWPal - A System for Analysis and Synthesis of Web Pages.
5
12
Aster servers: How to make Aster more widely available.
0
13
An Object-Oriented Intermediate Code Representation for the Development of Parallelization Tools.
2
14 39
15 1
16 131
17 49
18 15
19 9
20 2

About Mukkai S. Krishnamoorthy

Mukkai S. Krishnamoorthy is a scholar working on Hardware and Architecture, Artificial Intelligence and Modeling and Simulation, having authored 88 papers that have together received 1.3k indexed citations. Recurring topics across this work include Web Data Mining and Analysis (10 papers), Complexity and Algorithms in Graphs (9 papers) and Semantic Web and Ontologies (9 papers). The work is most often cited by research in Computational Theory and Mathematics (372 citations), Computational Mathematics (9 citations) and Computer Networks and Communications (343 citations). Mukkai S. Krishnamoorthy has collaborated with scholars based in United States, India and Poland. Frequent co-authors include Narsingh Deo, Balakrishnan Krishnamurthy, Hilmi Yıldırım, George Nagy, Sharad Seth, Mahesh Viswanathan, Erich Kaltofen, B. David Saunders, Malik Magdon‐Ismail and Mark Goldberg. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine 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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