Jun Heo

690 citations
60 papers · 520 · h-index 12

Impact in

Papers in

Jun Heo

52 papers receiving 501 citations

Peers

Jun Heo
Comparison fields: 5 of 87
  • Biochemistry 34
  • Computer Networks and Communications 105
  • Epidemiology 126
  • Complementary and alternative medicine 25
  • Immunology 54
Replace Yue Shang with:
Yue Shang China
Akira Yamamoto Japan
Andrés J. Gonzalez Norway
Rajesh Kumar Pathak India
Kazuhide Fukushima Japan
Kuan‐Rong Lee Taiwan
Yu Tian China
Sheng Sun China
Fei Yuan China
Changguang Wang China
Jun Heo relative to Yue Shang China Yue Shang's profile →
Citations per field
00.5×3.5×
Yue Shang · 1×
Citations per year

Countries citing papers authored by Jun Heo

Since Specialization
Citations

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

Fields of papers citing papers by Jun Heo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 60 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201368
2 201345
3 201541
4 200741
5 201330
6 201920
7 201119
8 202118
9 201418
10 201315
11 201214
12 200213
13 202411
14 201410
15 20229
16 20019
17 20169
18 20208
19 20148
20 20108

About Jun Heo

Jun Heo is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Atomic and Molecular Physics, and Optics and Epidemiology, having authored 60 papers that have together received 520 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (23 papers), Quantum Information and Cryptography (17 papers), Influenza Virus Research Studies (11 papers), Advanced Wireless Communication Techniques (11 papers), Quantum Mechanics and Applications (9 papers), Respiratory viral infections research (9 papers), Quantum-Dot Cellular Automata (7 papers) and Wireless Communication Networks Research (7 papers). The work is most often cited by research in Biochemistry (34 citations), Computer Networks and Communications (105 citations), Epidemiology (126 citations), Complementary and alternative medicine (25 citations) and Immunology (54 citations). Jun Heo has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Sehee Park, Jin Il Kim, Joon‐Yong Bae, Man‐Seong Park, Ilseob Lee, Sung‐il Kim, Il‐Min Kim, K.M. Chugg, Mee Sook Park and Sae-Young Chung. Their work appears in journals such as Quantum Information Processing, The Journal of Microbiology, Biochemical and Biophysical Research Communications, Physical Review A and PLoS ONE.

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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