Jiang Bian

3.5k total citations · 1 hit paper
78 papers, 1.6k citations indexed

About

Jiang Bian is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Jiang Bian has authored 78 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 27 papers in Computer Vision and Pattern Recognition and 22 papers in Information Systems. Recurrent topics in Jiang Bian's work include Topic Modeling (12 papers), Expert finding and Q&A systems (8 papers) and Recommender Systems and Techniques (8 papers). Jiang Bian is often cited by papers focused on Topic Modeling (12 papers), Expert finding and Q&A systems (8 papers) and Recommender Systems and Techniques (8 papers). Jiang Bian collaborates with scholars based in China, United States and United Kingdom. Jiang Bian's co-authors include Eugene Agichtein, Yandong Liu, Hongyuan Zha, Haoyi Xiong, Ding Zhou, Dejing Dou, Xuhong Li, Ji Liu, Xingjian Li and Xuanyu Wu and has published in prestigious journals such as The Journal of Chemical Physics, The Science of The Total Environment and Journal of Hydrology.

In The Last Decade

Jiang Bian

75 papers receiving 1.5k citations

Hit Papers

Interpretable deep learning: interpretation, interpretabi... 2022 2026 2023 2024 2022 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
    2022 246 citations Xuhong Li, Haoyi Xiong et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiang Bian China 17 934 700 326 238 102 78 1.6k
Bert Huang United States 18 766 0.8× 176 0.3× 160 0.5× 228 1.0× 112 1.1× 50 1.3k
Minghui Qiu China 23 1.5k 1.6× 676 1.0× 229 0.7× 501 2.1× 56 0.5× 85 2.1k
Gjergji Kasneci Germany 18 1.3k 1.4× 469 0.7× 78 0.2× 215 0.9× 258 2.5× 61 2.0k
Qi Su China 21 1.3k 1.4× 403 0.6× 78 0.2× 162 0.7× 193 1.9× 137 1.9k
Yifan Zhu China 19 455 0.5× 239 0.3× 97 0.3× 103 0.4× 80 0.8× 106 987
Meng Han China 20 530 0.6× 719 1.0× 145 0.4× 135 0.6× 503 4.9× 157 1.7k
Kan Li China 19 429 0.5× 270 0.4× 97 0.3× 197 0.8× 115 1.1× 99 984
Shlomo Geva Australia 16 661 0.7× 336 0.5× 46 0.1× 212 0.9× 80 0.8× 155 1.1k
Erheng Zhong Hong Kong 18 587 0.6× 372 0.5× 58 0.2× 293 1.2× 89 0.9× 28 1.1k

Countries citing papers authored by Jiang Bian

Since Specialization
Citations

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

Fields of papers citing papers by Jiang Bian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiang Bian

This figure shows the co-authorship network connecting the top 25 collaborators of Jiang Bian. A scholar is included among the top collaborators of Jiang Bian 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 Jiang Bian. Jiang Bian 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
1.
Zhou, Tiancheng, Dong Yang, Kaleb Smith, et al.. (2025). From image to report: automating lung cancer screening interpretation and reporting with vision-language models. Journal of Biomedical Informatics. 171. 104931–104931.
2.
Li, Yuchen, Xuhong Li, Jiang Bian, et al.. (2025). Pre-trained molecular language models with random functional group masking. 1(1). 1 indexed citations
3.
Bian, Jiang, Xiaolong Hu, Liangsheng Shi, et al.. (2024). Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain. Agricultural Water Management. 295. 108735–108735. 4 indexed citations
4.
Li, Yuchen, Haoyi Xiong, Linghe Kong, et al.. (2024). GS2P: a generative pre-trained learning to rank model with over-parameterization for web-scale search. Machine Learning. 113(8). 5331–5349. 5 indexed citations
5.
6.
Gray, Nick, Jiang Bian, Alex Wang, et al.. (2023). GLARE: A Dataset for Traffic Sign Detection in Sun Glare. IEEE Transactions on Intelligent Transportation Systems. 24(11). 12323–12330. 18 indexed citations
7.
Bian, Jiang, et al.. (2023). PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation. 4262–4272. 4 indexed citations
8.
Li, Yuchen, Haoyi Xiong, Linghe Kong, et al.. (2023). GS2P: A Generative Pre-trained Learning to Rank Model with Over-parameterization for Web-Scale Search. 30. 1–2. 1 indexed citations
9.
Hu, Xiaolong, et al.. (2023). Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation. The Science of The Total Environment. 871. 161919–161919. 6 indexed citations
10.
Xiong, Haoyi, et al.. (2020). COMO: Efficient Deep Neural Networks Expansion With COnvolutional MaxOut. IEEE Transactions on Multimedia. 23. 1722–1730. 7 indexed citations
11.
Wei, Pengfei, et al.. (2020). MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler.. Neural Information Processing Systems. 33. 14463–14474. 2 indexed citations
12.
Xiong, Haoyi, Wei Cheng, Jiang Bian, et al.. (2018). <inline-formula> <tex-math notation="LaTeX">$\mathcal{DBSDA}$ </tex-math> </inline-formula>: Lowering the Bound of Misclassification Rate for Sparse Linear Discriminant Analysis via Model Debiasing. IEEE Transactions on Neural Networks and Learning Systems. 30(3). 707–717. 6 indexed citations
13.
Sun, Shizhao, Wei Chen, Jiang Bian, Xiaoguang Liu, & Tie‐Yan Liu. (2018). Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning. 721–729. 8 indexed citations
14.
Bian, Jiang, Haoyi Xiong, Wei Cheng, et al.. (2017). Multi-party Sparse Discriminant Learning. 745–750. 4 indexed citations
15.
Cui, Qing, et al.. (2014). Co-learning of Word Representations and Morpheme Representations. International Conference on Computational Linguistics. 141–150. 44 indexed citations
16.
Tian, Fei, Hanjun Dai, Jiang Bian, et al.. (2014). A Probabilistic Model for Learning Multi-Prototype Word Embeddings. International Conference on Computational Linguistics. 151–160. 68 indexed citations
17.
Cui, Qing, et al.. (2014). Learning Effective Word Embedding using Morphological Word Similarity.. arXiv (Cornell University). 5 indexed citations
18.
Bian, Jiang, Yi Chang, Yun Fu, & Wen-Yen Chen. (2012). Learning to blend vitality rankings from heterogeneous social networks. Neurocomputing. 97. 390–397. 4 indexed citations
19.
Zha, Hongyuan & Jiang Bian. (2010). Contextualized web search: query-dependent ranking and social media search. 1 indexed citations
20.
Bian, Jiang, Tie‐Yan Liu, Tao Qin, & Hongyuan Zha. (2010). Ranking with query-dependent loss for web search. 141–150. 16 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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