Mingxin Gan

961 citations
67 papers · 672 · h-index 16

Impact in

Papers in

Mingxin Gan

60 papers receiving 660 citations

Peers

Mingxin Gan
Comparison fields: 5 of 95
  • Information Systems 372
  • Transportation 70
  • Artificial Intelligence 247
  • Statistical and Nonlinear Physics 60
  • Management Science and Operations Research 54
Replace Kamal K. Bharadwaj with:
Kamal K. Bharadwaj India
Bharat Bhasker India
Yi Fang United States
Beidou Wang China
Qinke Peng China
María Salamó Spain
Fabrizio Marozzo Italy
Sujoy Bag India
Cihan Kaleli Türkiye
Mingxin Gan relative to Kamal K. Bharadwaj India Kamal K. Bharadwaj's profile →
Citations per field
00.5×3.3×
Kamal K. Bharadwaj · 1×
Citations per year

Countries citing papers authored by Mingxin Gan

Since Specialization
Citations

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

Fields of papers citing papers by Mingxin Gan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201371
2 201168
3 201345
4 201332
5 202226
6 201425
7 202125
8 202122
9 201922
10 201920
11 202220
12 201520
13 202219
14 202119
15 202216
16 202316
17 202015
18 201514
19 202312
20 202412

About Mingxin Gan

Mingxin Gan is a scholar working on Information Systems, Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 67 papers that have together received 672 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (44 papers), Advanced Graph Neural Networks (20 papers), Machine Learning in Bioinformatics (11 papers), Complex Network Analysis Techniques (11 papers), Bioinformatics and Genomic Networks (9 papers), Human Mobility and Location-Based Analysis (8 papers), Topic Modeling (8 papers) and Advanced Bandit Algorithms Research (6 papers). The work is most often cited by research in Information Systems (372 citations), Transportation (70 citations), Artificial Intelligence (247 citations), Statistical and Nonlinear Physics (60 citations) and Management Science and Operations Research (54 citations). Mingxin Gan has collaborated with scholars based in China, United States and India. Frequent co-authors include Rui Jiang, Peng He, Ling Gao, H. Y. Zhang, Shengquan Chen, Xiongtao Zhang, Hairong Lv, Jiaxin Wu, Qiao Liu and Lingling Yi. Their work appears in journals such as Expert Systems with Applications, World Wide Web, BMC Systems Biology, Information Processing & Management and ISPRS International Journal of Geo-Information.

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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