Wilfred Ng

4.4k citations
106 papers · 2.4k · h-index 26

Impact in

    • Data Management and Algorithms
    • Data Mining Algorithms and Applications
    • Recommender Systems and Techniques
    • Web Data Mining and Analysis

Papers in

Wilfred Ng

104 papers receiving 2.3k citations

Peers

Wilfred Ng
Comparison fields: 5 of 89
  • Signal Processing 740
  • Information Systems 1.1k
  • Computer Vision and Pattern Recognition 945
  • Artificial Intelligence 1.1k
  • Computer Science Applications 177
Replace Cecilia M. Procopiuc with:
Cecilia M. Procopiuc United States
Jingren Zhou United States
Raffaele Perego Italy
Lizhu Zhou China
Carlotta Domeniconi United States
Vanja Josifovski United States
Yuanyuan Tian United States
Rina Panigrahy‎ United States
Torsten Suel United States
H.-P. Kriegel Germany
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Citations per field
00.5×1.5×1.8×
Cecilia M. Procopiuc · 1×
Citations per year

Countries citing papers authored by Wilfred Ng

Since Specialization
Citations

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

Fields of papers citing papers by Wilfred Ng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 106 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007184
2 2014158
3 2012136
4 2014133
5 2015124
6 201988
7 200786
8 200882
9 201570
10 201569
11 201468
12 200941
13 201139
14 201338
15 200537
16 201433
17 201433
18 201631
19 200631
20 200730

About Wilfred Ng

Wilfred Ng is a scholar working on Information Systems, Signal Processing, Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 106 papers that have together received 2.4k indexed citations. Recurring topics across this work include Data Management and Algorithms (45 papers), Data Mining Algorithms and Applications (21 papers), Advanced Database Systems and Queries (20 papers), Web Data Mining and Analysis (15 papers), Rough Sets and Fuzzy Logic (12 papers), Topic Modeling (10 papers), Mobile Crowdsensing and Crowdsourcing (9 papers) and Graph Theory and Algorithms (9 papers). The work is most often cited by research in Signal Processing (740 citations), Information Systems (1.1k citations), Computer Vision and Pattern Recognition (945 citations), Artificial Intelligence (1.1k citations) and Computer Science Applications (177 citations). Wilfred Ng has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include James Cheng, Yiping Ke, Zhou Zhao, Da Yan, Qiong Fang, Kenneth Wai-Ting Leung, Jianlin Feng, Yi Lu, Lu An and Dik Lun Lee. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Knowledge and Information Systems, Proceedings of the VLDB Endowment, ACM Transactions on Database Systems and ACM Transactions on Internet Technology.

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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