Yongjun Hong

403 total citations
6 papers, 252 citations indexed

About

Yongjun Hong is a scholar working on Computer Vision and Pattern Recognition, Ocean Engineering and Artificial Intelligence. According to data from OpenAlex, Yongjun Hong has authored 6 papers receiving a total of 252 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 2 papers in Ocean Engineering and 2 papers in Artificial Intelligence. Recurrent topics in Yongjun Hong's work include Generative Adversarial Networks and Image Synthesis (3 papers), Hydraulic Fracturing and Reservoir Analysis (2 papers) and Digital Media Forensic Detection (2 papers). Yongjun Hong is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (3 papers), Hydraulic Fracturing and Reservoir Analysis (2 papers) and Digital Media Forensic Detection (2 papers). Yongjun Hong collaborates with scholars based in South Korea. Yongjun Hong's co-authors include Uiwon Hwang, Sungroh Yoon, Jaeyoon Yoo, Juhyun Kim and Hoonyoung Jeong and has published in prestigious journals such as Energy, ACM Computing Surveys and Computers & Geosciences.

In The Last Decade

Yongjun Hong

6 papers receiving 243 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yongjun Hong South Korea	4	123	85	29	26	21	6	252
Jyoti Prakash Sahoo India	6	86 0.7×	171 2.0×	20 0.7×	22 0.8×	22 1.0×	14	361
Changhong Liu China	9	211 1.7×	108 1.3×	20 0.7×	48 1.8×	26 1.2×	38	357
Qing Song China	8	113 0.9×	172 2.0×	29 1.0×	28 1.1×	28 1.3×	37	316
Abdulrahman Alruban Saudi Arabia	6	69 0.6×	80 0.9×	18 0.6×	23 0.9×	12 0.6×	23	255
Shan Sung Liew Malaysia	5	121 1.0×	87 1.0×	13 0.4×	29 1.1×	7 0.3×	6	275
Uiwon Hwang South Korea	8	141 1.1×	146 1.7×	39 1.3×	47 1.8×	24 1.1×	13	327
Jiongcheng Li China	7	117 1.0×	166 2.0×	15 0.5×	12 0.5×	11 0.5×	12	344
Yuxuan Luo China	4	82 0.7×	140 1.6×	13 0.4×	18 0.7×	14 0.7×	12	260
Jingbo Zhou China	10	200 1.6×	42 0.5×	16 0.6×	64 2.5×	20 1.0×	48	353

Countries citing papers authored by Yongjun Hong

Since Specialization

Citations

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

Fields of papers citing papers by Yongjun Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yongjun Hong

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

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Hong, Yongjun, et al.. (2024). Enhancing pressure gradient prediction in multi-phase flow through diverse well geometries of North American shale gas fields using deep learning. Energy. 290. 130291–130291. 3 indexed citations

2.

Hong, Yongjun, et al.. (2023). Improved prediction of shale gas productivity in the Marcellus shale using geostatistically generated well-log data and ensemble machine learning. Computers & Geosciences. 181. 105452–105452. 12 indexed citations

3.

Hong, Yongjun, Uiwon Hwang, Jaeyoon Yoo, & Sungroh Yoon. (2019). How Generative Adversarial Networks and Their Variants Work. ACM Computing Surveys. 52(1). 1–43. 214 indexed citations

4.

Hwang, Uiwon, et al.. (2018). Memory-Augmented Neural Networks for Knowledge Tracing from the Perspective of Learning and Forgetting. arXiv (Cornell University). 4 indexed citations

5.

Hong, Yongjun, Uiwon Hwang, Jaeyoon Yoo, & Sungroh Yoon. (2017). How Generative Adversarial Networks and its variants Work: An Overview of GAN. arXiv (Cornell University). 17 indexed citations

6.

Hong, Yongjun, Uiwon Hwang, Jaeyoon Yoo, & Sungroh Yoon. (2017). How Generative Adversarial Networks and Their Variants Work: An Overview of GAN. arXiv (Cornell University). 2 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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