Jingjun Bi

476 total citations
12 papers, 365 citations indexed

About

Jingjun Bi is a scholar working on Artificial Intelligence, Urban Studies and Electrical and Electronic Engineering. According to data from OpenAlex, Jingjun Bi has authored 12 papers receiving a total of 365 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 3 papers in Urban Studies and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Jingjun Bi's work include Imbalanced Data Classification Techniques (5 papers), Text and Document Classification Technologies (4 papers) and Advanced Graph Neural Networks (4 papers). Jingjun Bi is often cited by papers focused on Imbalanced Data Classification Techniques (5 papers), Text and Document Classification Technologies (4 papers) and Advanced Graph Neural Networks (4 papers). Jingjun Bi collaborates with scholars based in China, Spain and Sweden. Jingjun Bi's co-authors include Chongsheng Zhang, Gaojuan Fan, Hamido Fujita, Baojun Qiao, Shixin Xu, Enislay Ramentol, Paolo Soda, Fadi Dornaika, Salvador García and Weiping Ding and has published in prestigious journals such as Neurocomputing, Neural Networks and Knowledge-Based Systems.

In The Last Decade

Jingjun Bi

9 papers receiving 357 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jingjun Bi China 7 277 82 71 37 35 12 365
Bhagat Singh Raghuwanshi India 11 387 1.4× 120 1.5× 111 1.6× 28 0.8× 26 0.7× 19 463
Shang Zheng China 9 266 1.0× 53 0.6× 70 1.0× 72 1.9× 17 0.5× 24 376
Sarah Vluymans Belgium 12 328 1.2× 83 1.0× 60 0.8× 95 2.6× 19 0.5× 16 467
Josey Mathew Singapore 4 207 0.7× 48 0.6× 80 1.1× 28 0.8× 18 0.5× 8 307
Shakil Ahmed Bangladesh 8 137 0.5× 31 0.4× 36 0.5× 28 0.8× 23 0.7× 14 238
Shenkai Gu United Kingdom 6 295 1.1× 84 1.0× 23 0.3× 31 0.8× 11 0.3× 8 376
Lida Abdi Iran 7 336 1.2× 51 0.6× 129 1.8× 59 1.6× 39 1.1× 9 426
Ayça Deniz Türkiye 8 303 1.1× 87 1.1× 21 0.3× 29 0.8× 16 0.5× 15 436
Ali I. El-Desouky Egypt 9 186 0.7× 48 0.6× 65 0.9× 96 2.6× 23 0.7× 25 390
Longfei Li China 9 323 1.2× 70 0.9× 31 0.4× 99 2.7× 9 0.3× 28 432

Countries citing papers authored by Jingjun Bi

Since Specialization
Citations

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

Fields of papers citing papers by Jingjun Bi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingjun Bi

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

All Works

12 of 12 papers shown
1.
Liu, Jie, et al.. (2025). Research on hull form optimization at multiple speeds based on machine learning and ship model experiments. Engineering Applications of Artificial Intelligence. 160. 111882–111882.
2.
Dornaika, Fadi, et al.. (2025). Semi-supervised learning for multi-view and non-graph data using Graph Convolutional Networks. Neural Networks. 185. 107218–107218. 4 indexed citations
3.
Bi, Jingjun, et al.. (2025). Linear projection fused graph-based semi-supervised learning on multi-view data. Artificial Intelligence Review. 58(10).
4.
5.
Bi, Jingjun & Fadi Dornaika. (2023). Sample-weighted fused graph-based semi-supervised learning on multi-view data. Information Fusion. 104. 102175–102175. 10 indexed citations
6.
Dornaika, Fadi, Jingjun Bi, & Chongsheng Zhang. (2022). A unified deep semi-supervised graph learning scheme based on nodes re-weighting and manifold regularization. Neural Networks. 158. 188–196. 8 indexed citations
7.
Zhang, Chongsheng, Paolo Soda, Jingjun Bi, et al.. (2022). Correction to: An empirical study on the joint impact of feature selection and data resampling on imbalance classification. Applied Intelligence. 53(7). 8506–8506. 7 indexed citations
8.
Zhang, Chongsheng, Paolo Soda, Jingjun Bi, et al.. (2022). An empirical study on the joint impact of feature selection and data resampling on imbalance classification. Applied Intelligence. 24 indexed citations
9.
Zhang, Chongsheng, Jingjun Bi, Shixin Xu, et al.. (2019). Multi-Imbalance: An open-source software for multi-class imbalance learning. Knowledge-Based Systems. 174. 137–143. 137 indexed citations
10.
Bi, Jingjun & Chongsheng Zhang. (2018). An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme. Knowledge-Based Systems. 158. 81–93. 156 indexed citations
11.
Zhang, Chongsheng, Jingjun Bi, & Paolo Soda. (2017). Feature selection and resampling in class imbalance learning: Which comes first? An empirical study in the biological domain. 14. 933–938. 18 indexed citations
12.
Zhang, Chongsheng, Jingjun Bi, Changchang Liu, & Ke Chen. (2016). A parameter-free label propagation algorithm for person identification in stereo videos. Neurocomputing. 218. 72–78. 1 indexed citations

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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2026