Da Yin

36 total papers · 2.3k total citations
14 papers, 267 citations indexed

About

Da Yin is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Da Yin has authored 14 papers receiving a total of 267 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 3 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Da Yin's work include Topic Modeling (7 papers), Advanced Text Analysis Techniques (4 papers) and Sentiment Analysis and Opinion Mining (3 papers). Da Yin is often cited by papers focused on Topic Modeling (7 papers), Advanced Text Analysis Techniques (4 papers) and Sentiment Analysis and Opinion Mining (3 papers). Da Yin collaborates with scholars based in China, United States and Singapore. Da Yin's co-authors include Kai-Wei Chang, Liunian Harold Li, Cho‐Jui Hsieh, Mark Yatskar, Tao Yu, Dragomir Radev, Ahmed Hassan Awadallah, Yang Liu, Yansong Feng and Aslı Çelikyılmaz and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, International Journal of Computer Applications in Technology and arXiv (Cornell University).

In The Last Decade

Da Yin

12 papers receiving 255 citations

Author Peers

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

Author						Papers	Cites
Da Yin	238	77	17	13	9	14	267
Shauli Ravfogel	254 1.1×	79 1.0×	18 1.1×	7 0.5×	6 0.7×	16	304
R. Thomas McCoy	280 1.2×	56 0.7×	15 0.9×	12 0.9×	4 0.4×	18	332
Ruiying Geng	268 1.1×	70 0.9×	24 1.4×	10 0.8×	3 0.3×	10	280
Nikita Nangia	265 1.1×	73 0.9×	22 1.3×	3 0.2×	6 0.7×	10	287
Lintang Sutawika	193 0.8×	35 0.5×	14 0.8×	6 0.5×	8 0.9×	8	227
Patrick Xia	263 1.1×	60 0.8×	24 1.4×	7 0.5×	2 0.2×	15	282
David Uthus	188 0.8×	31 0.4×	41 2.4×	20 1.5×	4 0.4×	13	245
Haozhe Ji	282 1.2×	73 0.9×	36 2.1×	9 0.7×	5 0.6×	9	296
Benjamin Börschinger	256 1.1×	42 0.5×	26 1.5×	15 1.2×	4 0.4×	13	296
Xiangru Tang	228 1.0×	30 0.4×	15 0.9×	7 0.5×	9 1.0×	21	297

Countries citing papers authored by Da Yin

Since Specialization

Citations

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

Fields of papers citing papers by Da Yin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Da Yin

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