Chenglong Wang

1.6k citations
36 papers · 811 indexed · 1 hit paper · h-index 14
Topics
Topic Modeling (9 papers)Natural Language Processing Techniques (5 papers)Advanced Database Systems and Queries (4 papers)

In The Last Decade

Chenglong Wang

33 papers receiving 785 citations

Hit Papers

Multi-scale carbon emission characterization and predicti...20242026202520241020304050

Peers

Chenglong Wang
Comparison fields: 5 of 89
  • Artificial Intelligence 378
  • Computer Vision and Pattern Recognition 316
  • Information Systems 139
  • Signal Processing 120
  • Computer Networks and Communications 78
Replace P.W. Grant with:
P.W. Grant United Kingdom
Vittorio Castelli United States
Rafael M. Martins Sweden
Parikshit Ram United States
Roberto Ierusalimschy Brazil
Kang Zhang China
Krešimir Matković Austria
Linbo Qiao China
Chenglong Wang relative to P.W. Grant United Kingdom P.W. Grant's profile →
Citations per field
00.5×4.0×
P.W. Grant · 1×
Citations per year

Countries citing papers authored by Chenglong Wang

Since Specialization
Citations

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

Fields of papers citing papers by Chenglong Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenglong Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Chenglong Wang. A scholar is included among the top collaborators of Chenglong Wang 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 Chenglong Wang. Chenglong Wang 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 0
2 4
3 0
4 2
5 9
6 6
7 9
8 13
9 44
10 6
11 4
12 27
13 3
14
Execution-Guided Neural Program Decoding
15
15 15
16 10
17 107
18 0
19 2
20 68

About Chenglong Wang

Chenglong Wang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 36 papers that have together received 811 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (5 papers) and Advanced Database Systems and Queries (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (316 citations), Software (51 citations) and Artificial Intelligence (378 citations). Chenglong Wang has collaborated with scholars based in China, United States and Saudi Arabia. Frequent co-authors include Alvin Cheung, Rastislav Bodík, Feiping Nie, Xuelong Li, Bill Howe, Dominik Moritz, Jeffrey Heer, Greg L. Nelson, Adam M. Smith and Austin J. Herrema. Their work appears in journals such as IEEE Transactions on Information Theory, Applied Energy and Sensors.

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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