Lin Cui

855 citations
44 papers · 604 · h-index 14

Impact in

Papers in

Lin Cui

41 papers receiving 566 citations

Peers

Lin Cui
Comparison fields: 5 of 77
  • Information Systems and Management 244
  • Information Systems 284
  • Computer Networks and Communications 283
  • Statistical and Nonlinear Physics 70
  • Artificial Intelligence 160
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Johnson Apacible United States
Luke K. McDowell United States
Thomas Heinis United Kingdom
Vijay Gadepally United States
Martin Randles United Kingdom
Rob DeLine United States
Chris Stolte United States
V. Balaji India
Lida Li United States
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Citations per field
00.5×8.3×
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Citations per year

Countries citing papers authored by Lin Cui

Since Specialization
Citations

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

Fields of papers citing papers by Lin Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Lin Cui, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lin Cui Line = papers co-authored together Lin Cui links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009102
2 200859
3 200936
4 202033
5 202033
6 200732
7 201832
8 200924
9 201523
10 202021
11 200720
12 202016
13 201815
14 202513
15 202112
16 202112
17 201912
18 201710
19 20119
20 20149

About Lin Cui

Lin Cui is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics and Information Systems and Management, having authored 44 papers that have together received 604 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (11 papers), Scientific Computing and Data Management (9 papers), Distributed and Parallel Computing Systems (8 papers), Advanced Graph Neural Networks (8 papers), Recommender Systems and Techniques (7 papers), Sentiment Analysis and Opinion Mining (6 papers), Time Series Analysis and Forecasting (4 papers) and EEG and Brain-Computer Interfaces (4 papers). The work is most often cited by research in Information Systems and Management (244 citations), Information Systems (284 citations), Computer Networks and Communications (283 citations), Statistical and Nonlinear Physics (70 citations) and Artificial Intelligence (160 citations). Lin Cui has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Dechang Pi, Xubo Fei, Shiyong Lu, Jing Hua, Farshad Fotouhi, Artem Chebotko, Darshan Pai, Jia Wu, Chenglong Dai and Bentian Li. Their work appears in journals such as IEEE Access, Journal of Network and Computer Applications, Applied Soft Computing, IEEE Transactions on Services Computing and Information Sciences.

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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