David J. Haglin

73 total papers · 771 total citations
44 papers, 408 citations indexed

About

David J. Haglin is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, David J. Haglin has authored 44 papers receiving a total of 408 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Networks and Communications, 17 papers in Artificial Intelligence and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in David J. Haglin's work include Graph Theory and Algorithms (11 papers), Advanced Database Systems and Queries (8 papers) and Data Mining Algorithms and Applications (7 papers). David J. Haglin is often cited by papers focused on Graph Theory and Algorithms (11 papers), Advanced Database Systems and Queries (8 papers) and Data Mining Algorithms and Applications (7 papers). David J. Haglin collaborates with scholars based in United States, United Kingdom and Italy. David J. Haglin's co-authors include Richard J. Roiger, Jon Hakkila, Shankar M. Venkatesan, Charles A. Meegan, Timothy W. Giblin, Robert S. Mallozzi, John Feo, Geoffrey N. Pendleton, John Keane and W. S. Pačiesas and has published in prestigious journals such as Gastroenterology, The Astrophysical Journal and The Astrophysical Journal Supplement Series.

In The Last Decade

David J. Haglin

41 papers receiving 376 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
David J. Haglin	146	119	99	96	91	44	408
Xiaoyao Xie	115 0.8×	30 0.3×	164 1.7×	22 0.2×	82 0.9×	85	402
Qing Liu	220 1.5×	34 0.3×	89 0.9×	67 0.7×	91 1.0×	42	458
James J. Hunt	104 0.7×	82 0.7×	98 1.0×	44 0.5×	106 1.2×	31	368
Marcos V. S. Alves	93 0.6×	50 0.4×	122 1.2×	267 2.8×	22 0.2×	35	488
John W. Romein	104 0.7×	96 0.8×	189 1.9×	11 0.1×	66 0.7×	30	412
‬‬‬‬‬‬Eva Sciacca	57 0.4×	161 1.4×	72 0.7×	19 0.2×	38 0.4×	53	405
Yao Yao	196 1.3×	19 0.2×	56 0.6×	32 0.3×	53 0.6×	36	446
B. Earl Wells	58 0.4×	100 0.8×	136 1.4×	18 0.2×	34 0.4×	49	371
James Ang	139 1.0×	35 0.3×	153 1.5×	27 0.3×	78 0.9×	31	428
Marie‐Jeanne Lesot	231 1.6×	34 0.3×	24 0.2×	92 1.0×	60 0.7×	45	474

Countries citing papers authored by David J. Haglin

Since Specialization

Citations

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

Fields of papers citing papers by David J. Haglin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Haglin. 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 David J. Haglin. The network helps show where David J. Haglin may publish in the future.

Co-authorship network of co-authors of David J. Haglin

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

All Works

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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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