Nan Tang

150 papers receiving 3.8k citations

Peers

Nan Tang
Comparison fields: 5 of 115
  • Artificial Intelligence 2.4k
  • Management Science and Operations Research 1.8k
  • Computer Networks and Communications 1.1k
  • Information Systems 1.1k
  • Signal Processing 888
Replace Ihab F. Ilyas with:
Ihab F. Ilyas Canada
Xin Luna Dong United States
Themis Palpanas France
Wenfei Fan United Kingdom
Tim Kraska United States
Renée J. Miller Canada
Shuai Ma China
Amol Deshpande United States
Magdalena Bałazińska United States
Stefano Rizzi Italy
Nan Tang relative to Ihab F. Ilyas Canada Ihab F. Ilyas's profile →
Citations per field
00.5×10×
Ihab F. Ilyas · 1×
Citations per year

Countries citing papers authored by Nan Tang

Since Specialization
Citations

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

Fields of papers citing papers by Nan Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nan Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Nan Tang. A scholar is included among the top collaborators of Nan Tang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nan Tang. Nan Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 6
2 1
3 3
4 4
5 9
6 1
7 11
8 0
9 11
10
Relational Pretrained Transformers towards Democratizing Data Preparation [Vision].
2
11 40
12 97
13 5
14
The data civilizer system
63
15
Discriminative Bi-Term Topic Model for Headline-Based Social News Clustering
23
16 1
17
The Application of Project-teaching in the Portrait Photography Teaching
0
18
Accurate Measurement System for Downhole Turbine Electrical Machines
2
19
Design and Implementation of a Parallel Data Partitioning Algorithm for XML Data
0
20
Control system in rotary steering drilling tool based on state space method
5

About Nan Tang

Nan Tang is a scholar working on Management Science and Operations Research, Signal Processing and Artificial Intelligence, having authored 163 papers that have together received 4.0k indexed citations. Recurring topics across this work include Data Quality and Management (65 papers), Advanced Database Systems and Queries (37 papers) and Data Management and Algorithms (28 papers). The work is most often cited by research in Management Science and Operations Research (1.8k citations), Signal Processing (888 citations) and Artificial Intelligence (2.4k citations). Nan Tang has collaborated with scholars based in China, Qatar and United States. Frequent co-authors include Guoliang Li, Mourad Ouzzani, Wenfei Fan, Yuyu Luo, Xuedi Qin, Shuai Ma, Ihab F. Ilyas, Wenyuan Yu, Chengliang Chai and Paolo Papotti. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy and IEEE Transactions on Visualization and Computer Graphics.

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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