Dijun Luo

1.3k citations
36 papers · 758 · h-index 16

Impact in

Papers in

Dijun Luo

36 papers receiving 729 citations

Peers

Dijun Luo
Comparison fields: 5 of 114
  • Computational Mathematics 56
  • Computer Vision and Pattern Recognition 212
  • Artificial Intelligence 268
  • Computer Networks and Communications 158
  • Health Information Management 24
Replace Alioune Ngom with:
Alioune Ngom Canada
Chris Ding United States
Grigorios Tzortzis Greece
Ilya Safro United States
Mingming Sun China
Hongchang Gao United States
Grzegorz Świrszcz United States
Lili Pan China
Tiantian He China
Dijun Luo relative to Alioune Ngom Canada Alioune Ngom's profile →
Citations per field
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Alioune Ngom · 1×
Citations per year

Countries citing papers authored by Dijun Luo

Since Specialization
Citations

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

Fields of papers citing papers by Dijun Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201190
2 201268
3
Combining knowledge and data driven insights for identifying risk factors using electronic health records.
201265
4
Cauchy Graph Embedding
201158
5 201451
6 200840
7 201037
8 201135
9 200829
10 201127
11 201026
12 200926
13 201118
14 201218
15 201215
16
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach
201215
17 201215
18 202015
19 201014
20 202114

About Dijun Luo

Dijun Luo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mathematics, Computational Mechanics and Molecular Biology, having authored 36 papers that have together received 758 indexed citations. Recurring topics across this work include Face and Expression Recognition (11 papers), Sparse and Compressive Sensing Techniques (5 papers), Tensor decomposition and applications (5 papers), Neural Networks and Applications (4 papers), Advanced Graph Neural Networks (4 papers), Image Retrieval and Classification Techniques (4 papers), Remote-Sensing Image Classification (3 papers) and Greenhouse Technology and Climate Control (3 papers). The work is most often cited by research in Computational Mathematics (56 citations), Computer Vision and Pattern Recognition (212 citations), Artificial Intelligence (268 citations), Computer Networks and Communications (158 citations) and Health Information Management (24 citations). Dijun Luo has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Heng Huang, Chris Ding, Guihai Chen, Xiaobing Wu, Xiaojun Zhu, Feiping Nie, Tao Li, Marianthi Markatou, Fei Wang and Jianying Hu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Physics Letters, American Journal Of Pathology, Neurocomputing and Knowledge and Information Systems.

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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