Hongbin Pei

1.2k total citations
23 papers, 423 citations indexed

About

Hongbin Pei is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hongbin Pei has authored 23 papers receiving a total of 423 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Information Systems and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hongbin Pei's work include Recommender Systems and Techniques (7 papers), Advanced Graph Neural Networks (6 papers) and COVID-19 epidemiological studies (4 papers). Hongbin Pei is often cited by papers focused on Recommender Systems and Techniques (7 papers), Advanced Graph Neural Networks (6 papers) and COVID-19 epidemiological studies (4 papers). Hongbin Pei collaborates with scholars based in China, Hong Kong and United Kingdom. Hongbin Pei's co-authors include Bo Yang, Wenhui Li, Jialun Liu, Yifan Sun, Yi Yang, Feng Zhu, Jiming Liu, Wenjie Li, Yu Lei and Hechang Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Expert Systems with Applications and IEEE Access.

In The Last Decade

Hongbin Pei

20 papers receiving 411 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hongbin Pei China	11	208	145	78	45	42	23	423
Yifan Zhu China	11	69 0.3×	47 0.3×	52 0.7×	51 1.1×	51 1.2×	67	387
Sheng Feng China	11	74 0.4×	79 0.5×	25 0.3×	33 0.7×	29 0.7×	30	317
Chao Fan China	10	89 0.4×	49 0.3×	38 0.5×	17 0.4×	9 0.2×	36	276
Md Tahmid Rashid United States	11	61 0.3×	87 0.6×	44 0.6×	43 1.0×	4 0.1×	32	384
Nur Ezlin Zamri Malaysia	10	85 0.4×	258 1.8×	63 0.8×	9 0.2×	16 0.4×	31	375
Bernardo Morales-Castañeda Mexico	10	76 0.4×	400 2.8×	21 0.3×	28 0.6×	27 0.6×	22	580
Nirnay Ghosh India	10	25 0.1×	123 0.8×	76 1.0×	10 0.2×	27 0.6×	31	352
Kenny Daniel United States	4	148 0.7×	70 0.5×	12 0.2×	89 2.0×	7 0.2×	6	283

Countries citing papers authored by Hongbin Pei

Since Specialization

Citations

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

Fields of papers citing papers by Hongbin Pei

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongbin Pei

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

All Works

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20 of 20 papers shown

Xu, Lixin, Ruiheng Zhang, Jin Zhang, et al.. (2025). ADP: Graph Adaptive Pooling Based on Edge Understanding With Graph Pooling Information Bottleneck. IEEE Transactions on Consumer Electronics. 72(1). 692–704.

Wang, Changyu, et al.. (2025). An unsupervised fusion framework of generation and retrieval for entity search. Expert Systems with Applications. 286. 128106–128106.

Zhen, Jie, Gang Zheng, Junhua Huang, et al.. (2025). Deformation and Control of Metro Structures during Asymmetric Bilateral Zero-Distance Excavations. International Journal of Geomechanics. 25(10).

Pei, Hongbin, Pinghui Wang, Jing Tao, et al.. (2024). Memory Disagreement: A Pseudo-Labeling Measure from Training Dynamics for Semi-supervised Graph Learning. 434–445. 2 indexed citations

Wang, Xiayu, et al.. (2024). Data-driven smoothing approaches for interest modeling in recommendation systems. Expert Systems with Applications. 249. 123524–123524. 3 indexed citations

Pei, Hongbin, et al.. (2024). HAGO-Net: Hierarchical Geometric Message Passing for Molecular Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(13). 14572–14580. 3 indexed citations

Ma, Jie, et al.. (2024). Robust Visual Question Answering: Datasets, Methods, and Future Challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(8). 5575–5594. 16 indexed citations

Pei, Hongbin, et al.. (2023). Deep click interest network for reranking hotels. Engineering Applications of Artificial Intelligence. 130. 107675–107675. 2 indexed citations

Zhang, Chunxu, Ximing Li, Hongbin Pei, et al.. (2023). LaenNet: Learning robust GCNs by propagating labels. Neural Networks. 168. 652–664. 4 indexed citations

10.

Liu, Jialun, Yifan Sun, Feng Zhu, et al.. (2022). Learning Memory-Augmented Unidirectional Metrics for Cross-modality Person Re-identification. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 19344–19353. 131 indexed citations

11.

Liu, Jialun, et al.. (2021). Feature Cloud: Improving Deep Visual Recognition With Probabilistic Feature Augmentation. IEEE Transactions on Circuits and Systems for Video Technology. 32(7). 4122–4137. 9 indexed citations

12.

Lei, Yu, et al.. (2020). Reinforcement Learning based Recommendation with Graph Convolutional Q-network. 1757–1760. 31 indexed citations

13.

Pei, Hongbin, Bo Yang, Jiming Liu, & Kevin Chen–Chuan Chang. (2020). Active Surveillance via Group Sparse Bayesian Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(3). 1133–1148. 51 indexed citations

14.

Lei, Yu, Zhitao Wang, Wenjie Li, Hongbin Pei, & Quanyu Dai. (2020). Social Attentive Deep Q-Networks for Recommender Systems. IEEE Transactions on Knowledge and Data Engineering. 34(5). 2443–2457. 15 indexed citations

15.

Yang, Bo, et al.. (2020). Neural Explainable Recommender Model Based on Attributes and Reviews. Journal of Computer Science and Technology. 35(6). 1446–1460. 3 indexed citations

16.

Lei, Yu, Zhitao Wang, Wenjie Li, & Hongbin Pei. (2019). Social Attentive Deep Q-network for Recommendation. 1189–1192. 21 indexed citations

17.

Chen, Hechang, Bo Yang, Hongbin Pei, & Jiming Liu. (2018). Next Generation Technology for Epidemic Prevention and Control: Data-Driven Contact Tracking. IEEE Access. 7. 2633–2642. 47 indexed citations

18.

Pei, Hongbin, Bo Yang, Jiming Liu, & Lei Dong. (2018). Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 12 indexed citations

19.

Yang, Bo, Hongbin Pei, Hechang Chen, Jiming Liu, & Shang Xia. (2016). Characterizing and Discovering Spatiotemporal Social Contact Patterns for Healthcare. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39(8). 1532–1546. 10 indexed citations

20.

Yang, Bo, Hongbin Pei, Hechang Chen, Jiming Liu, & Shang Xia. (2014). Modeling and Mining Spatiotemporal Social Contact of Metapopulation from Heterogeneous Data. 70. 630–639. 4 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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