Yan Song

144 total papers · 3.2k total citations
80 papers, 1.8k citations indexed

About

Yan Song is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Yan Song has authored 80 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 7 papers in Molecular Biology. Recurrent topics in Yan Song's work include Topic Modeling (55 papers), Natural Language Processing Techniques (50 papers) and Multimodal Machine Learning Applications (10 papers). Yan Song is often cited by papers focused on Topic Modeling (55 papers), Natural Language Processing Techniques (50 papers) and Multimodal Machine Learning Applications (10 papers). Yan Song collaborates with scholars based in China, United States and Hong Kong. Yan Song's co-authors include Yuanhe Tian, Xiang Wan, Zhihong Chen, Guimin Chen, Tsung‐Hui Chang, Fei Xia, Fei Xia, Tong Zhang, Shizhe Diao and Jing Li and has published in prestigious journals such as IEEE Transactions on Medical Imaging, BMC Bioinformatics and Applied Catalysis A General.

In The Last Decade

Yan Song

70 papers receiving 1.8k citations

Author Peers

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

Author Last Decade Papers Cites
Yan Song 1.5k 389 184 144 140 80 1.8k
Yining Wang 671 0.4× 192 0.5× 101 0.5× 28 0.2× 157 1.1× 127 1.5k
Yang Liu 1.8k 1.2× 630 1.6× 110 0.6× 11 0.1× 141 1.0× 96 2.3k
Liang Ding 797 0.5× 297 0.8× 44 0.2× 35 0.2× 69 0.5× 163 1.9k
Lingling Zhang 544 0.4× 330 0.8× 55 0.3× 55 0.4× 88 0.6× 111 1.4k
Fandong Meng 1.1k 0.7× 371 1.0× 60 0.3× 6 0.0× 90 0.6× 126 1.5k
Qianqian Xie 536 0.4× 40 0.1× 333 1.8× 41 0.3× 141 1.0× 103 1.7k
Tianlin Zhang 376 0.3× 130 0.3× 97 0.5× 18 0.1× 42 0.3× 63 1.3k
Bei Chen 272 0.2× 131 0.3× 76 0.4× 41 0.3× 66 0.5× 152 1.4k
Arman Cohan 1.8k 1.2× 128 0.3× 573 3.1× 50 0.3× 272 1.9× 73 2.3k
Christoph M. Friedrich 459 0.3× 238 0.6× 344 1.9× 163 1.1× 76 0.5× 116 1.5k

Countries citing papers authored by Yan Song

Since Specialization
Citations

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

Fields of papers citing papers by Yan Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yan Song

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