Xiaojun Chen

4.2k total citations · 1 hit paper
122 papers, 2.9k citations indexed

About

Xiaojun Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Xiaojun Chen has authored 122 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Artificial Intelligence, 43 papers in Computer Vision and Pattern Recognition and 25 papers in Information Systems. Recurrent topics in Xiaojun Chen's work include Face and Expression Recognition (23 papers), Topic Modeling (20 papers) and Text and Document Classification Technologies (14 papers). Xiaojun Chen is often cited by papers focused on Face and Expression Recognition (23 papers), Topic Modeling (20 papers) and Text and Document Classification Technologies (14 papers). Xiaojun Chen collaborates with scholars based in China, United States and Hong Kong. Xiaojun Chen's co-authors include Joshua Zhexue Huang, Min Yang, Feiping Nie, Yunming Ye, Zhou Zhao, Salman Salloum, Xiaofei Xu, Ruslan Dautov, Qingyao Wu and Wenting Tu and has published in prestigious journals such as Nature Communications, Nano Letters and Journal of Applied Physics.

In The Last Decade

Xiaojun Chen

118 papers receiving 2.7k citations

Hit Papers

Big data analytics on Apache Spark 2016 2026 2019 2022 2016 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaojun Chen China 31 1.3k 948 393 255 235 122 2.9k
Min Zhang China 29 1.5k 1.1× 476 0.5× 679 1.7× 241 0.9× 304 1.3× 249 2.9k
Dipanjan Chakraborty India 30 441 0.3× 729 0.8× 849 2.2× 1.1k 4.4× 438 1.9× 104 3.2k
Jacques Wainer Brazil 28 557 0.4× 416 0.4× 513 1.3× 231 0.9× 207 0.9× 114 2.5k
Adam Prügel‐Bennett United Kingdom 25 1.1k 0.8× 436 0.5× 408 1.0× 212 0.8× 176 0.7× 96 2.3k
Xiaoping Chen China 30 753 0.6× 432 0.5× 96 0.2× 540 2.1× 244 1.0× 231 3.0k
Ivan Titov United Kingdom 30 3.4k 2.6× 887 0.9× 442 1.1× 51 0.2× 66 0.3× 106 4.5k
Chun‐Ying Huang Taiwan 34 490 0.4× 1.3k 1.4× 587 1.5× 1.9k 7.5× 560 2.4× 144 3.8k
Yi Chen China 22 555 0.4× 612 0.6× 304 0.8× 356 1.4× 167 0.7× 139 2.1k
William F. Punch United States 27 2.3k 1.7× 901 1.0× 505 1.3× 170 0.7× 118 0.5× 81 3.8k
Xing Wu China 19 446 0.3× 245 0.3× 150 0.4× 135 0.5× 328 1.4× 126 1.5k

Countries citing papers authored by Xiaojun Chen

Since Specialization
Citations

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

Fields of papers citing papers by Xiaojun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaojun Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaojun Chen. A scholar is included among the top collaborators of Xiaojun Chen 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 Xiaojun Chen. Xiaojun Chen 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.
Chen, Xiaojun, et al.. (2024). A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models. Knowledge-Based Systems. 299. 111968–111968. 8 indexed citations
2.
Yang, Zemao, Jiquan Chen, Zhigang Dai, et al.. (2024). Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute. Environmental Pollution. 362. 124918–124918. 2 indexed citations
3.
4.
Li, Wenjing, Yan Kou, Xiaojun Chen, et al.. (2024). Partial order relation–based gene ontology embedding improves protein function prediction. Briefings in Bioinformatics. 25(2). 6 indexed citations
5.
Qiu, Liping, et al.. (2024). Multi-Level Cross-Modal Alignment for Image Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. 38(13). 14695–14703. 1 indexed citations
6.
Chen, Xiaojun, et al.. (2024). Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy. Nature Communications. 15(1). 4677–4677. 14 indexed citations
7.
Liu, Zichuan, Xiaojun Chen, Zhigang Xia, et al.. (2023). A self-powered electro-coagulation system afforded by flexible electromagnetic flag wind generators for efficient removal of arsenic from water. Nano Energy. 114. 108648–108648. 4 indexed citations
8.
Chen, Xiaojun & Huan Liu. (2023). Blockchain Technology Participates in the Path and Mode Optimization of Supply Chain Finance. 3(2). 36–40. 1 indexed citations
9.
Zhang, Qin, Shangsi Chen, Dongkuan Xu, et al.. (2023). A Survey for Efficient Open Domain Question Answering. 14447–14465. 14 indexed citations
10.
Chen, Jintai, et al.. (2022). Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(4). 2434–2444. 9 indexed citations
11.
Yang, Min, Chengming Li, Ying Shen, et al.. (2020). Hierarchical Human-Like Deep Neural Networks for Abstractive Text Summarization. IEEE Transactions on Neural Networks and Learning Systems. 32(6). 2744–2757. 42 indexed citations
12.
Yang, Min, Wenpeng Yin, Qiang Qu, et al.. (2019). Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification. IEEE Transactions on Affective Computing. 12(3). 761–775. 36 indexed citations
13.
Zhao, Wei, Benyou Wang, Min Yang, et al.. (2019). Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training. IEEE Transactions on Cybernetics. 50(11). 4680–4693. 49 indexed citations
14.
Yang, Min, et al.. (2019). An Advanced Deep Generative Framework for Temporal Link Prediction in Dynamic Networks. IEEE Transactions on Cybernetics. 50(12). 4946–4957. 45 indexed citations
15.
Wang, Shuai, et al.. (2019). Modal wavefront reconstruction to obtain Zernike coefficient with no cross coupling in lateral shearing measurement. Guangdian gongcheng. 46(5). 180273. 2 indexed citations
16.
Salloum, Salman, Joshua Zhexue Huang, Yulin He, & Xiaojun Chen. (2018). An Asymptotic Ensemble Learning Framework for Big Data Analysis. IEEE Access. 7. 3675–3693. 16 indexed citations
17.
Yang, Min, Wei Zhao, Wei Xu, et al.. (2018). Multitask Learning for Cross-Domain Image Captioning. IEEE Transactions on Multimedia. 21(4). 1047–1061. 101 indexed citations
18.
Salloum, Salman, et al.. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics. 1(3-4). 145–164. 261 indexed citations breakdown →
19.
Chen, Xiaojun, et al.. (2015). A Reversed-Typicality Effect in Pictures but Not in Written Words in Deaf and Hard of Hearing Adolescents. American annals of the deaf. 160(1). 48–59. 2 indexed citations
20.
Chen, Xiaojun. (2009). A comprehensive review of the theories and practices of emergency management capability assessment. Journal of Yanshan University. 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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