Ling Ding

491 citations
42 papers · 315 indexed · h-index 9

Impact in

Papers in

Ling Ding

36 papers receiving 297 citations

Peers

Ling Ding
Comparison fields: 5 of 96
  • Civil and Structural Engineering 69
  • Artificial Intelligence 71
  • Hardware and Architecture 14
  • Computer Vision and Pattern Recognition 42
  • Media Technology 15
Replace Pietro Cassará with:
Pietro Cassará Italy
Tim Farnham United Kingdom
Amir Shabani Canada
G. Saranya India
Ted Tsung-Te Lai Taiwan
Philip Virgil Astillo South Korea
Xiaopeng Fan China
Nipun Batra India
Semih Aslan United States
Anam Nawaz Khan South Korea
Ling Ding relative to Pietro Cassará Italy Pietro Cassará's profile →
Citations per field
00.5×1.5×2.4×
Pietro Cassará · 1×
Citations per year

Countries citing papers authored by Ling Ding

Since Specialization
Citations

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

Fields of papers citing papers by Ling Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202180
2 201661
3 202119
4 201517
5 199114
6 202012
7 20219
8 20229
9 20238
10 20188
11 20208
12 20207
13 20187
14 20137
15 19866
16 20215
17 20234
18 20233
19 20193
20 20243

About Ling Ding

Ling Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Information Systems and Computer Networks and Communications, having authored 42 papers that have together received 315 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Advanced Graph Neural Networks (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Cloud Computing and Resource Management (4 papers), Remote Sensing and Land Use (4 papers), Image Processing Techniques and Applications (3 papers), Recommender Systems and Techniques (3 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Civil and Structural Engineering (69 citations), Artificial Intelligence (71 citations), Hardware and Architecture (14 citations), Computer Vision and Pattern Recognition (42 citations) and Media Technology (15 citations). Ling Ding has collaborated with scholars based in China, New Zealand and United States. Frequent co-authors include Dean Zhao, Weikuan Jia, Xu Yang, Mohd Rosli Mohd Hasan, Jinchao Guan, Vincent C. S. Lee, Xiaoyun Cheng, Zhanping You, Xiaojun Chen and Yang Xiang. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Expert Systems with Applications, Multimedia Tools and Applications, Information Sciences and Dyes and Pigments.

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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