Deqing Wang

2.3k total citations · 1 hit paper
71 papers, 1.5k citations indexed

About

Deqing Wang is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Deqing Wang has authored 71 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Artificial Intelligence, 26 papers in Information Systems and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Deqing Wang's work include Topic Modeling (24 papers), Text and Document Classification Technologies (22 papers) and Recommender Systems and Techniques (14 papers). Deqing Wang is often cited by papers focused on Topic Modeling (24 papers), Text and Document Classification Technologies (22 papers) and Recommender Systems and Techniques (14 papers). Deqing Wang collaborates with scholars based in China, United States and Australia. Deqing Wang's co-authors include Fuzhen Zhuang, Yongchun Zhu, Hui Zhang, Fuzhen Zhuang, Pengpeng Zhao, Victor S. Sheng, Yanchi Liu, Xianglong Liu, Jiajie Xu and Jun Li and has published in prestigious journals such as Bioinformatics, Pattern Recognition and Aquaculture.

In The Last Decade

Deqing Wang

64 papers receiving 1.4k citations

Hit Papers

Aligning Domain-Specific Distribution and Classifier for ... 2019 2026 2021 2023 2019 50 100 150 200 250
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. Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources
    2019 278 citations Fuzhen Zhuang, Deqing Wang et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deqing Wang China 19 1.0k 507 394 100 71 71 1.5k
Shlomo Geva Australia 16 661 0.6× 336 0.7× 212 0.5× 61 0.6× 73 1.0× 155 1.1k
Xiaoming Zhang China 22 867 0.9× 237 0.5× 531 1.3× 45 0.5× 63 0.9× 113 1.5k
Pengfei Liu China 23 2.0k 1.9× 236 0.5× 316 0.8× 58 0.6× 47 0.7× 80 2.3k
Ning Ding China 13 1.2k 1.1× 379 0.7× 268 0.7× 80 0.8× 28 0.4× 33 1.7k
Yi He China 18 617 0.6× 285 0.6× 375 1.0× 102 1.0× 89 1.3× 115 1.4k
Donghai Guan China 19 523 0.5× 236 0.5× 389 1.0× 24 0.2× 38 0.5× 87 1.1k
Yanru Qu China 11 777 0.8× 473 0.9× 474 1.2× 78 0.8× 48 0.7× 16 1.2k
Li Dong China 18 2.3k 2.3× 366 0.7× 643 1.6× 79 0.8× 37 0.5× 63 2.8k

Countries citing papers authored by Deqing Wang

Since Specialization
Citations

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

Fields of papers citing papers by Deqing Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deqing Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Deqing Wang. A scholar is included among the top collaborators of Deqing Wang 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 Deqing Wang. Deqing Wang 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.
Zhang, Zhao, et al.. (2025). FairDgcl: Fairness-Aware Recommendation With Dynamic Graph Contrastive Learning. IEEE Transactions on Knowledge and Data Engineering. 37(9). 5230–5242. 1 indexed citations
2.
Chen, Yiwen, et al.. (2025). One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence. 39(12). 12300–12308.
3.
Su, Dianbin, et al.. (2025). Enhancing Rubus idaeus L. maturity detection and classification: A YOLO-CG-CPCA network model for efficient feature extraction and precise analysis. Postharvest Biology and Technology. 230. 113800–113800. 1 indexed citations
4.
Zhang, Fuwei, et al.. (2024). Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement. IEEE Transactions on Knowledge and Data Engineering. 36(11). 7115–7128. 3 indexed citations
5.
6.
Jiang, Ting, Shaohan Huang, Zhongzhi Luan, Deqing Wang, & Fuzhen Zhuang. (2024). Scaling Sentence Embeddings with Large Language Models. 3182–3196. 8 indexed citations
7.
Zhuang, Fuzhen, Ruobing Xie, Hengshu Zhu, et al.. (2024). A survey on causal inference for recommendation. The Innovation. 5(2). 100590–100590. 14 indexed citations
8.
Zhao, Pengpeng, et al.. (2023). Feature-Aware Contrastive Learning With Bidirectional Transformers for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering. 36(12). 8192–8205. 1 indexed citations
9.
Zhuang, Fuzhen, et al.. (2023). Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation. 565–575. 2 indexed citations
10.
Yao, Kaichun, et al.. (2023). Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph. arXiv (Cornell University). 1721–1730. 1 indexed citations
11.
Jiang, Ting, et al.. (2022). Exploiting Global and Local Hierarchies for Hierarchical Text Classification. 4030–4039. 10 indexed citations
12.
Jiang, Ting, Jian Jiao, Shaohan Huang, et al.. (2022). PromptBERT: Improving BERT Sentence Embeddings with Prompts. 8826–8837. 82 indexed citations
13.
Wang, Zeya, Yang Ni, Baoyu Jing, et al.. (2021). DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering. IEEE Transactions on Neural Networks and Learning Systems. 33(12). 7610–7620. 14 indexed citations
14.
Zhao, Xiaowei, et al.. (2020). A neural topic model with word vectors and entity vectors for short texts. Information Processing & Management. 58(2). 102455–102455. 40 indexed citations
15.
Wang, Deqing. (2019). Extracting Meaningful EEG Features Using Constrained Tensor Decomposition. Jyväskylä University Digital Archive (University of Jyväskylä). 1 indexed citations
16.
Zhang, Weifan, Hui Zhang, Yuan Zuo, & Deqing Wang. (2015). Modeling Both Coarse-Grained and Fine-Grained Topics in Massive Text Data. 378–383. 2 indexed citations
17.
Zhang, Hui, Deqing Wang, Li Wang, Zhuming Bi, & Yong Chen. (2013). A semantics-based method for clustering of Chinese web search results. Enterprise Information Systems. 8(1). 147–165. 9 indexed citations
18.
Wang, Deqing & Hui Zhang. (2013). Inverse-Category-Frequency Based Supervised Term Weighting Schemes for Text Categorization *. arXiv (Cornell University). 29(2). 209–225. 41 indexed citations
19.
20.
Wang, Deqing & Hui Zhang. (2013). Support-vector-based iteratively adjusted centroid classifier for text categorization. Beijing Hangkong Hangtian Daxue xuebao. 39(2). 269. 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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