Xiuwen Yi

3.2k total citations · 3 hit papers
23 papers, 2.2k citations indexed

About

Xiuwen Yi is a scholar working on Artificial Intelligence, Building and Construction and Environmental Engineering. According to data from OpenAlex, Xiuwen Yi has authored 23 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 8 papers in Building and Construction and 7 papers in Environmental Engineering. Recurrent topics in Xiuwen Yi's work include Traffic Prediction and Management Techniques (8 papers), Air Quality Monitoring and Forecasting (7 papers) and Human Mobility and Location-Based Analysis (5 papers). Xiuwen Yi is often cited by papers focused on Traffic Prediction and Management Techniques (8 papers), Air Quality Monitoring and Forecasting (7 papers) and Human Mobility and Location-Based Analysis (5 papers). Xiuwen Yi collaborates with scholars based in China, United States and Germany. Xiuwen Yi's co-authors include Yu Zheng, Junbo Zhang, Ruiyuan Li, Tianrui Li, Dekang Qi, Songyu Ke, Yuxuan Liang, Ming Li, Eric Chang and Zhaoyuan Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Hydrogen Energy and Information Sciences.

In The Last Decade

Xiuwen Yi

23 papers receiving 2.2k citations

Hit Papers

DNN-based prediction model for spatio-temporal data 2016 2026 2019 2022 2016 2018 2018 100 200 300 400
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. DNN-based prediction model for spatio-temporal data
    2016 478 citations Junbo Zhang, Yu Zheng et al. profile →
  2. Predicting citywide crowd flows using deep spatio-temporal residual networks
    2018 392 citations Junbo Zhang, Yu Zheng et al. Artificial Intelligence profile →
  3. GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction
    2018 371 citations Songyu Ke, Junbo Zhang et al. profile →

Peers

Xiuwen Yi
Shengdong Du China
Jun-Seok Oh United States
Dekang Qi China
Xiucheng Li China
Luliang Tang China
Zonghan Wu Australia
Jie Feng China
Shengnan Guo China
Zhimin Tao China
Shengdong Du China
Xiuwen Yi
Citations per year, relative to Xiuwen Yi Xiuwen Yi (= 1×) peers Shengdong Du

Countries citing papers authored by Xiuwen Yi

Since Specialization
Citations

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

Fields of papers citing papers by Xiuwen Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiuwen Yi

This figure shows the co-authorship network connecting the top 25 collaborators of Xiuwen Yi. A scholar is included among the top collaborators of Xiuwen Yi 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 Xiuwen Yi. Xiuwen Yi 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.
Huang, Yanyong, Minghui Lu, Wei Huang, Xiuwen Yi, & Tianrui Li. (2025). TIME-FS: Joint Learning of Tensorial Incomplete Multi-View Unsupervised Feature Selection and Missing-View Imputation. Proceedings of the AAAI Conference on Artificial Intelligence. 39(16). 17503–17510. 1 indexed citations
2.
Huang, Yanyong, Yuxin Cai, Xiuwen Yi, et al.. (2023). C2IMUFS: Complementary and Consensus Learning-Based Incomplete Multi-View Unsupervised Feature Selection. IEEE Transactions on Knowledge and Data Engineering. 35(10). 10681–10694. 24 indexed citations
3.
Liu, Hui, et al.. (2023). Synergistic effects of Mo2C/CeO2 nanoparticles anchored on nitrogen-doped carbon as an efficiently electrocatalyst for hydrogen evolution reaction. International Journal of Hydrogen Energy. 48(62). 23831–23841. 19 indexed citations
4.
Yi, Xiuwen, et al.. (2023). Neural Clustering and Ranking Approach for Gas-Theft Suspect Detection. SHILAP Revista de lepidopterología. 3(2). 68–79. 1 indexed citations
5.
Huang, Yanyong, Kejun Guo, Xiuwen Yi, Zhong Li, & Tianrui Li. (2023). Incremental unsupervised feature selection for dynamic incomplete multi-view data. Information Fusion. 96. 312–327. 12 indexed citations
6.
Huang, Yanyong, et al.. (2022). Matrix representation of the conditional entropy for incremental feature selection on multi-source data. Information Sciences. 591. 263–286. 21 indexed citations
7.
Huang, Yanyong, et al.. (2022). T-copula and Wasserstein distance-based stochastic neighbor embedding. Knowledge-Based Systems. 243. 108431–108431. 6 indexed citations
8.
Wang, Zhaoyuan, Zheyi Pan, Shun Chen, et al.. (2022). Shortening passengers' travel time: A dynamic metro train scheduling approach using deep reinforcement learning. IEEE Transactions on Knowledge and Data Engineering. 1–1. 3 indexed citations
9.
Yi, Xiuwen, Yanyong Huang, Songyu Ke, et al.. (2021). Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach. IEEE Transactions on Knowledge and Data Engineering. 34(12). 5796–5808. 6 indexed citations
10.
Yi, Xiuwen, et al.. (2020). Predicting Fine-Grained Air Quality Based on Deep Neural Networks. IEEE Transactions on Big Data. 8(5). 1326–1339. 30 indexed citations
11.
Yi, Xiuwen, et al.. (2020). You Are How You Use. 2885–2892. 4 indexed citations
12.
Yi, Xiuwen, et al.. (2019). CityTraffic. 2665–2671. 19 indexed citations
13.
Yu, Yanwei, Xianfeng Tang, Huaxiu Yao, Xiuwen Yi, & Zhenhui Li. (2019). Citywide Traffic Volume Inference with Surveillance Camera Records. IEEE Transactions on Big Data. 7(6). 900–912. 14 indexed citations
14.
Zhang, Junbo, Yu Zheng, Dekang Qi, et al.. (2018). Predicting citywide crowd flows using deep spatio-temporal residual networks. Artificial Intelligence. 259. 147–166. 392 indexed citations breakdown →
15.
Yi, Xiuwen, Junbo Zhang, Zhaoyuan Wang, Tianrui Li, & Yu Zheng. (2018). Deep Distributed Fusion Network for Air Quality Prediction. 965–973. 209 indexed citations
16.
Meng, Chuishi, Xiuwen Yi, Lü Su, Jing Gao, & Yu Zheng. (2017). City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories. 1–10. 74 indexed citations
17.
Zhang, Junbo, Yu Zheng, Dekang Qi, Ruiyuan Li, & Xiuwen Yi. (2016). DNN-based prediction model for spatio-temporal data. 1–4. 478 indexed citations breakdown →
18.
Bao, Jie, Ruiyuan Li, Xiuwen Yi, & Yu Zheng. (2016). Managing massive trajectories on the cloud. 1–10. 40 indexed citations
19.
Zheng, Yu, et al.. (2016). A Gaussian Bayesian model to identify spatio-temporal causalities for air pollution based on urban big data. The HKU Scholars Hub (University of Hong Kong). 3–8. 17 indexed citations
20.
Zheng, Yu, Xiuwen Yi, Ming Li, et al.. (2015). Forecasting Fine-Grained Air Quality Based on Big Data. 2267–2276. 339 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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