Ling Wei

1.8k citations
59 papers · 1.2k · h-index 19

Impact in

Papers in

Ling Wei

50 papers receiving 1.2k citations

Peers

Ling Wei
Comparison fields: 5 of 76
  • Computational Theory and Mathematics 1.1k
  • Management Science and Operations Research 269
  • Artificial Intelligence 689
  • Information Systems 449
  • Signal Processing 205
Replace Keyun Qin with:
Keyun Qin China
Martin Štěpnička Czechia
Anna Wilbik Netherlands
Haibo Jiang China
Cassio P. de Campos Switzerland
Zhi‐Hong Deng China
Péter Müller Switzerland
Mohua Banerjee India
Robert Susmaga Poland
Quinlan United States
Ling Wei relative to Keyun Qin China Keyun Qin's profile →
Citations per field
00.5×1.5×1.8×
Keyun Qin · 1×
Citations per year

Countries citing papers authored by Ling Wei

Since Specialization
Citations

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

Fields of papers citing papers by Ling Wei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ling Wei, 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 Ling Wei Line = papers co-authored together Ling Wei links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015195
2 2016101
3 200894
4 201993
5 201084
6 201984
7 201668
8 201943
9 201839
10 201436
11 201931
12 201927
13 201427
14 201725
15 202124
16 202023
17 201820
18 201619
19 202319
20 201516

About Ling Wei

Ling Wei is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Information Systems, Signal Processing and Political Science and International Relations, having authored 59 papers that have together received 1.2k indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (43 papers), Data Mining Algorithms and Applications (22 papers), Advanced Computational Techniques and Applications (13 papers), Data Management and Algorithms (8 papers), Semantic Web and Ontologies (8 papers), Multi-Criteria Decision Making (6 papers), Advanced Algebra and Logic (6 papers) and Text and Document Classification Technologies (6 papers). The work is most often cited by research in Computational Theory and Mathematics (1.1k citations), Management Science and Operations Research (269 citations), Artificial Intelligence (689 citations), Information Systems (449 citations) and Signal Processing (205 citations). Ling Wei has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Jianjun Qi, Ting Qian, Wen‐Xiu Zhang, Huilai Zhi, Lin Liu, Zhen Wang, Yiyu Yao, Xiaoli He, Yanhong She and Jinhai Li. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Knowledge-Based Systems, International Journal of Approximate Reasoning, Information Sciences and Cognitive Computation.

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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