Ning Wang

120 papers receiving 916 citations

Peers

Ning Wang
Comparison fields: 5 of 138
  • Human-Computer Interaction 118
  • Signal Processing 179
  • Health Informatics 18
  • Computer Science Applications 68
  • Computer Vision and Pattern Recognition 181
Replace Jaiteg Singh with:
Jaiteg Singh India
Jürgen Ziegler Germany
Danilo Caivano Italy
Maarten van Someren Netherlands
Jafreezal Jaafar Malaysia
Cynthia Matuszek United States
Rui Xing China
Jianguo Ding Sweden
Minsuk Kahng United States
Paolo Buono Italy
Ning Wang relative to Jaiteg Singh India Jaiteg Singh's profile →
Citations per field
00.5×4.2×
Jaiteg Singh · 1×
Citations per year

Countries citing papers authored by Ning Wang

Since Specialization
Citations

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

Fields of papers citing papers by Ning Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 146 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016117
2 201863
3 201262
4 202346
5 201845
6 200343
7 201243
8 202140
9 202134
10 202134
11 201330
12 201824
13 202318
14 202116
15 201314
16 201713
17
Cross-Domain Speech Disfluency Detection
201012
18 202011
19 20239
20 20259

About Ning Wang

Ning Wang is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Signal Processing and Management Science and Operations Research, having authored 146 papers that have together received 965 indexed citations. Recurring topics across this work include Data Quality and Management (17 papers), Caching and Content Delivery (16 papers), Data Management and Algorithms (15 papers), Web Data Mining and Analysis (15 papers), Advanced Database Systems and Queries (14 papers), Peer-to-Peer Network Technologies (13 papers), Semantic Web and Ontologies (12 papers) and Topic Modeling (7 papers). The work is most often cited by research in Human-Computer Interaction (118 citations), Signal Processing (179 citations), Health Informatics (18 citations), Computer Science Applications (68 citations) and Computer Vision and Pattern Recognition (181 citations). Ning Wang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Ahmed A. Abd El‐Latif, Qiong Li, Xiamu Niu, James C. Lester, Jialiang Peng, Qiaozhu Mei, Xuan Lü, Gang Huang, Qian Li and Wei Ai. Their work appears in journals such as Neurocomputing, Information Sciences, Expert Systems with Applications, Computers and Electronics in Agriculture and Knowledge-Based Systems.

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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