Xin Song

178 total papers · 3.0k total citations
135 papers, 2.1k citations indexed

About

Xin Song is a scholar working on Molecular Biology, Food Science and Genetics. According to data from OpenAlex, Xin Song has authored 135 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Molecular Biology, 38 papers in Food Science and 22 papers in Genetics. Recurrent topics in Xin Song's work include Probiotics and Fermented Foods (33 papers), Gut microbiota and health (19 papers) and Genomics and Phylogenetic Studies (12 papers). Xin Song is often cited by papers focused on Probiotics and Fermented Foods (33 papers), Gut microbiota and health (19 papers) and Genomics and Phylogenetic Studies (12 papers). Xin Song collaborates with scholars based in China, United States and Czechia. Xin Song's co-authors include Lianzhong Ai, Zhiqiang Xiong, Yongjun Xia, Guangqiang Wang, He Huang, Sheng Yang, Linghui Kong, Yijin Yang, Shengfang Ge and Xianqun Fan and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Xin Song

128 papers receiving 2.1k citations

Author Peers

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

Author Last Decade Papers Cites
Xin Song 1.3k 567 252 246 239 135 2.1k
Jiajia Song 1.1k 0.8× 487 0.9× 130 0.5× 236 1.0× 155 0.6× 130 2.0k
Lei Yuan 1.4k 1.0× 855 1.5× 125 0.5× 157 0.6× 126 0.5× 139 2.4k
Kiminori Matsubara 1.1k 0.8× 208 0.4× 199 0.8× 170 0.7× 298 1.2× 75 2.4k
Kozo Asano 1.4k 1.1× 579 1.0× 128 0.5× 474 1.9× 425 1.8× 109 2.9k
Shu Quan 1.4k 1.1× 907 1.6× 262 1.0× 446 1.8× 143 0.6× 88 2.9k
Chatchai Muanprasat 1.2k 0.9× 248 0.4× 242 1.0× 541 2.2× 311 1.3× 121 2.9k
Joanne K. Tobacman 938 0.7× 376 0.7× 246 1.0× 249 1.0× 211 0.9× 108 2.8k
Alejandra Ochoa‐Zarzosa 881 0.7× 479 0.8× 160 0.6× 146 0.6× 222 0.9× 95 2.3k
Jian Ji 1.4k 1.0× 380 0.7× 126 0.5× 187 0.8× 349 1.5× 88 2.6k
Keitarou Kimura 1.0k 0.8× 288 0.5× 279 1.1× 350 1.4× 162 0.7× 78 2.0k

Countries citing papers authored by Xin Song

Since Specialization
Citations

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

Fields of papers citing papers by Xin Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xin Song

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