Hailong Jin

78 total papers · 505 total citations
37 papers, 313 citations indexed

About

Hailong Jin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Global and Planetary Change. According to data from OpenAlex, Hailong Jin has authored 37 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Global and Planetary Change. Recurrent topics in Hailong Jin's work include Natural Language Processing Techniques (7 papers), Topic Modeling (7 papers) and Advanced Graph Neural Networks (4 papers). Hailong Jin is often cited by papers focused on Natural Language Processing Techniques (7 papers), Topic Modeling (7 papers) and Advanced Graph Neural Networks (4 papers). Hailong Jin collaborates with scholars based in China, United States and Germany. Hailong Jin's co-authors include Juanzi Li, Lei Hou, Yong Zhang, Richard Teague, Yinxi Zhang, Kang Sun, Urs P. Kreuter, Yongzhong Fan, Tong Wang and Dingwu Zhou and has published in prestigious journals such as Journal of Environmental Management, American Mineralogist and IEEE Transactions on Robotics.

In The Last Decade

Hailong Jin

32 papers receiving 299 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Hailong Jin 111 59 38 29 28 37 313
Honghua Liu 39 0.4× 52 0.9× 13 0.3× 10 0.3× 4 0.1× 28 359
Xinming Lu 49 0.4× 31 0.5× 12 0.3× 72 2.5× 38 1.4× 37 318
Markos Avlonitis 48 0.4× 40 0.7× 27 0.7× 17 0.6× 19 0.7× 58 346
Hang Yu 86 0.8× 12 0.2× 12 0.3× 86 3.0× 25 0.9× 57 365
S. K. Mittal 43 0.4× 42 0.7× 10 0.3× 10 0.3× 34 1.2× 36 290
Lhoussaine Masmoudi 56 0.5× 32 0.5× 28 0.7× 5 0.2× 127 4.5× 62 362
Dachuan Wang 56 0.5× 15 0.3× 8 0.2× 94 3.2× 6 0.2× 36 358
Siyuan Li 28 0.3× 35 0.6× 22 0.6× 5 0.2× 13 0.5× 31 283
Xiaotian Pan 53 0.5× 9 0.2× 23 0.6× 35 1.2× 12 0.4× 34 272
Yafei Huang 103 0.9× 18 0.3× 5 0.1× 14 0.5× 8 0.3× 25 285

Countries citing papers authored by Hailong Jin

Since Specialization
Citations

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

Fields of papers citing papers by Hailong Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hailong Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Hailong Jin. A scholar is included among the top collaborators of Hailong Jin 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 Hailong Jin. Hailong Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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