Murray Browne

2.0k total citations · 1 hit paper
7 papers, 1.2k citations indexed

About

Murray Browne is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Murray Browne has authored 7 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Information Systems, 2 papers in Artificial Intelligence and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Murray Browne's work include Complex Network Analysis Techniques (1 paper), Advanced Text Analysis Techniques (1 paper) and Sparse and Compressive Sensing Techniques (1 paper). Murray Browne is often cited by papers focused on Complex Network Analysis Techniques (1 paper), Advanced Text Analysis Techniques (1 paper) and Sparse and Compressive Sensing Techniques (1 paper). Murray Browne collaborates with scholars based in United States. Murray Browne's co-authors include Michael W. Berry, Amy N. Langville, V. Paúl Pauca, Robert J. Plemmons and John C. Martin and has published in prestigious journals such as Computational Statistics & Data Analysis, Applied Numerical Mathematics and Computational and Mathematical Organization Theory.

In The Last Decade

Murray Browne

7 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Algorithms and applications for approximate nonnegative matrix factorization

2006 1.0k citations Michael W. Berry, Murray Browne et al. Computational Statistics & Data Analysis profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Murray Browne United States	6	326	324	252	172	142	7	1.2k
Tao Xiong United States	16	572 1.8×	493 1.5×	181 0.7×	165 1.0×	134 0.9×	40	1.5k
David K. Hammond United States	9	685 2.1×	512 1.6×	121 0.5×	267 1.6×	160 1.1×	22	1.7k
Mark E. Oxley United States	18	611 1.9×	321 1.0×	209 0.8×	90 0.5×	45 0.3×	115	1.7k
Shinichi Nakajima Japan	17	1.0k 3.1×	580 1.8×	217 0.9×	147 0.9×	93 0.7×	105	2.0k
Pai‐Hsuen Chen Taiwan	4	502 1.5×	431 1.3×	84 0.3×	103 0.6×	175 1.2×	4	1.3k
Genevieve Orr United States	8	777 2.4×	550 1.7×	185 0.7×	90 0.5×	68 0.5×	26	1.7k
Maya R. Gupta United States	22	811 2.5×	618 1.9×	275 1.1×	107 0.6×	87 0.6×	93	1.9k
Andrew R. Webb United Kingdom	11	631 1.9×	505 1.6×	216 0.9×	48 0.3×	124 0.9×	24	1.7k
Clayton Scott United States	30	907 2.8×	338 1.0×	118 0.5×	160 0.9×	105 0.7×	111	2.4k
Evrim Acar Denmark	24	512 1.6×	248 0.8×	453 1.8×	372 2.2×	319 2.2×	60	2.4k

Countries citing papers authored by Murray Browne

Since Specialization

Citations

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

Fields of papers citing papers by Murray Browne

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Murray Browne

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

All Works

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7 of 7 papers shown

1.

Berry, Michael W., et al.. (2008). Document classification techniques for automated technology readiness level analysis. Journal of the American Society for Information Science and Technology. 59(4). 675–680. 13 indexed citations

2.

Martin, John C., et al.. (2007). Out-of-core SVD performance for document indexing. Applied Numerical Mathematics. 57(11-12). 1230–1239. 5 indexed citations

3.

Berry, Michael W. & Murray Browne. (2006). Lecture Notes in Data Mining. WORLD SCIENTIFIC eBooks. 40 indexed citations

4.

Berry, Michael W., Murray Browne, Amy N. Langville, V. Paúl Pauca, & Robert J. Plemmons. (2006). Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics & Data Analysis. 52(1). 155–173. 1022 indexed citations breakdown →

5.

Berry, Michael W. & Murray Browne. (2005). Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools), Second Edition. Society for Industrial and Applied Mathematics eBooks. 13 indexed citations

6.

Berry, Michael W. & Murray Browne. (2005). Understanding Search Engines. Society for Industrial and Applied Mathematics eBooks. 65 indexed citations

7.

Berry, Michael W. & Murray Browne. (2005). Email Surveillance Using Non-negative Matrix Factorization. Computational and Mathematical Organization Theory. 11(3). 249–264. 88 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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