Sa-Kwang Song

97 total papers · 845 total citations
43 papers, 414 citations indexed

About

Sa-Kwang Song is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sa-Kwang Song has authored 43 papers receiving a total of 414 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 13 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sa-Kwang Song's work include Natural Language Processing Techniques (8 papers), Topic Modeling (7 papers) and Context-Aware Activity Recognition Systems (6 papers). Sa-Kwang Song is often cited by papers focused on Natural Language Processing Techniques (8 papers), Topic Modeling (7 papers) and Context-Aware Activity Recognition Systems (6 papers). Sa-Kwang Song collaborates with scholars based in South Korea, Switzerland and United States. Sa-Kwang Song's co-authors include Hanmin Jung, Qing Li, Qixu Gong, Yuanzhu Chen, Tiejun Wang, Zhangxi Lin, Sung Hyon Myaeng, Jae‐Won Jang, Minho Kim and Seungwoo Lee and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Access and Remote Sensing.

In The Last Decade

Sa-Kwang Song

34 papers receiving 385 citations

Author Peers

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

Author						Papers	Cites
Sa-Kwang Song	110	100	90	84	64	43	414
Yan Li	79 0.7×	50 0.5×	52 0.6×	38 0.5×	43 0.7×	22	407
Deepak Kumar	51 0.5×	72 0.7×	86 1.0×	41 0.5×	106 1.7×	49	445
Yanxing Hu	58 0.5×	136 1.4×	38 0.4×	29 0.3×	37 0.6×	30	454
Hang Gao	43 0.4×	133 1.3×	43 0.5×	65 0.8×	24 0.4×	54	379
Dimitrios Michail	33 0.3×	107 1.1×	34 0.4×	70 0.8×	49 0.8×	39	470
Nan Zhu	28 0.3×	47 0.5×	101 1.1×	101 1.2×	48 0.8×	37	472
Shahram Golzari	36 0.3×	77 0.8×	94 1.0×	50 0.6×	19 0.3×	26	420
Wei Cui	87 0.8×	81 0.8×	50 0.6×	177 2.1×	16 0.3×	51	479
Hexiang Bai	39 0.4×	75 0.8×	123 1.4×	28 0.3×	20 0.3×	34	434
Sibghat Ullah Bazai	29 0.3×	86 0.9×	38 0.4×	66 0.8×	14 0.2×	41	426

Countries citing papers authored by Sa-Kwang Song

Since Specialization

Citations

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

Fields of papers citing papers by Sa-Kwang Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sa-Kwang Song. 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 Sa-Kwang Song. The network helps show where Sa-Kwang Song may publish in the future.

Co-authorship network of co-authors of Sa-Kwang Song

This figure shows the co-authorship network connecting the top 25 collaborators of Sa-Kwang Song. A scholar is included among the top collaborators of Sa-Kwang Song 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 Sa-Kwang Song. Sa-Kwang Song 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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