Xingyi Song

78 total papers · 869 total citations
31 papers, 444 citations indexed

About

Xingyi Song is a scholar working on Artificial Intelligence, Sociology and Political Science and Information Systems. According to data from OpenAlex, Xingyi Song has authored 31 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 9 papers in Sociology and Political Science and 5 papers in Information Systems. Recurrent topics in Xingyi Song's work include Topic Modeling (13 papers), Natural Language Processing Techniques (9 papers) and Misinformation and Its Impacts (8 papers). Xingyi Song is often cited by papers focused on Topic Modeling (13 papers), Natural Language Processing Techniques (9 papers) and Misinformation and Its Impacts (8 papers). Xingyi Song collaborates with scholars based in United Kingdom, China and Switzerland. Xingyi Song's co-authors include Diana Maynard, Ye Jiang, Johann Petrak, Kalina Bontcheva, Matthew Gilbert, Linwei He, Angus Roberts, Junjie Wang, Masaaki Kitano and Lelun Jiang and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and PLoS ONE.

In The Last Decade

Xingyi Song

28 papers receiving 427 citations

Author Peers

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

Author Last Decade Papers Cites
Xingyi Song 157 94 69 62 56 31 444
Wentao Zheng 81 0.5× 28 0.3× 109 1.6× 52 0.8× 147 2.6× 31 466
Robert Craven 125 0.8× 78 0.8× 57 0.8× 6 0.1× 63 1.1× 36 414
Wenbo Jiang 184 1.2× 11 0.1× 44 0.6× 4 0.1× 52 0.9× 38 446
Nan Yu 163 1.0× 33 0.4× 17 0.2× 16 0.3× 21 0.4× 40 403
Wei Dang 58 0.4× 6 0.1× 39 0.6× 11 0.2× 44 0.8× 19 482
Shuyi Zhang 93 0.6× 11 0.1× 17 0.2× 24 0.4× 32 0.6× 30 441
Gang Yan 51 0.3× 7 0.1× 34 0.5× 27 0.4× 19 0.3× 28 387
Stefano Foresti 68 0.4× 25 0.3× 44 0.6× 86 1.4× 37 0.7× 24 416
Michael Köller 60 0.4× 84 0.9× 25 0.4× 10 0.2× 37 0.7× 34 451
Liqin Wang 54 0.3× 9 0.1× 40 0.6× 7 0.1× 40 0.7× 20 466

Countries citing papers authored by Xingyi Song

Since Specialization
Citations

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

Fields of papers citing papers by Xingyi Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xingyi Song

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