Jing Gao

9.9k citations
164 papers · 6.4k indexed · 6 hit papers · h-index 37
Topics
Topic Modeling (27 papers)Mobile Crowdsensing and Crowdsourcing (24 papers)Anomaly Detection Techniques and Applications (15 papers)
Partner nations
United StatesChinaCanada

In The Last Decade

Jing Gao

154 papers receiving 6.2k citations

Hit Papers

EANN201320262017202120182013201420202014200400600

Peers

Jing Gao
Comparison fields: 5 of 170
  • Artificial Intelligence 4.0k
  • Computer Science Applications 1.2k
  • Information Systems 1.2k
  • Computer Vision and Pattern Recognition 1.1k
  • Sociology and Political Science 967
Replace Victor S. Sheng with:
Victor S. Sheng United States
Lianyong Qi China
Xiaokang Zhou Japan
Dit‐Yan Yeung Hong Kong
Wanchun Dou China
Xuyun Zhang China
Kai Zheng China
Xiaolong Xu China
Yong Xiang Australia
Lars Schmidt-Thieme Germany
Jing Gao relative to Victor S. Sheng United States Victor S. Sheng's profile →
Citations per field
00.5×4.7×
Victor S. Sheng · 1×
Citations per year

Countries citing papers authored by Jing Gao

Since Specialization
Citations

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

Fields of papers citing papers by Jing Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jing Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Jing Gao. A scholar is included among the top collaborators of Jing Gao 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 Jing Gao. Jing Gao 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 1
2 0
3 5
4 4
5 0
6 1
7 16
8 6
9 16
10 12
11 3
12 19
13
A Survey on Causal Inferencebreakdown →
257
14 1
15 10
16 8
17 17
18 94
19
Representation Learning for Treatment Effect Estimation from Observational Data
66
20 10

About Jing Gao

Jing Gao is a scholar working on Computer Science Applications, Computational Mathematics and Artificial Intelligence, having authored 164 papers that have together received 6.4k indexed citations. Recurring topics across this work include Topic Modeling (27 papers), Mobile Crowdsensing and Crowdsourcing (24 papers) and Anomaly Detection Techniques and Applications (15 papers). The work is most often cited by research in Computer Science Applications (1.2k citations), Artificial Intelligence (4.0k citations) and Computational Mathematics (52 citations). Jing Gao has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Jiawei Han, Yaliang Li, Lü Su, Manish Gupta, Fenglong Ma, Charų C. Aggarwal, Jialu Liu, Chi Wang, Aidong Zhang and Zhikui Chen. Their work appears in journals such as PLoS ONE, IEEE Transactions on Geoscience and Remote Sensing and IEEE Access.

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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