Dongjin Yu

2.5k total citations
115 papers, 1.6k citations indexed

About

Dongjin Yu is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Dongjin Yu has authored 115 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Information Systems, 46 papers in Artificial Intelligence and 35 papers in Computer Networks and Communications. Recurrent topics in Dongjin Yu's work include Recommender Systems and Techniques (31 papers), Software Engineering Research (19 papers) and Service-Oriented Architecture and Web Services (19 papers). Dongjin Yu is often cited by papers focused on Recommender Systems and Techniques (31 papers), Software Engineering Research (19 papers) and Service-Oriented Architecture and Web Services (19 papers). Dongjin Yu collaborates with scholars based in China, Australia and Japan. Dongjin Yu's co-authors include Dongjing Wang, Xi Zheng, C. van Achterberg, Guandong Xu, Alireza Jolfaei, Shuiguang Deng, Gaochao Xu, Yong Li, Yipeng Zhou and Xin Zhang and has published in prestigious journals such as IEEE Communications Surveys & Tutorials, Expert Systems with Applications and IEEE Access.

In The Last Decade

Dongjin Yu

103 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dongjin Yu China	21	734	583	382	258	178	115	1.6k
Xiaoguang Liu China	19	450 0.6×	608 1.0×	793 2.1×	231 0.9×	75 0.4×	168	1.7k
Zhuo Wang China	20	288 0.4×	575 1.0×	201 0.5×	98 0.4×	26 0.1×	132	2.2k
Huai Liu China	22	656 0.9×	367 0.6×	358 0.9×	79 0.3×	36 0.2×	138	2.1k
George Okeyo Kenya	15	189 0.3×	354 0.6×	284 0.7×	345 1.3×	10 0.1×	40	965
Liviu Panait United States	18	132 0.2×	1.1k 1.9×	501 1.3×	158 0.6×	37 0.2×	27	2.3k
Luiz F. Bittencourt Brazil	26	1.6k 2.2×	247 0.4×	2.4k 6.2×	308 1.2×	111 0.6×	143	2.9k
Julio Hernández-Castro United Kingdom	19	520 0.7×	494 0.8×	455 1.2×	415 1.6×	16 0.1×	110	1.5k
Avi Pfeffer United States	25	451 0.6×	1.3k 2.2×	768 2.0×	152 0.6×	13 0.1×	89	2.8k
Shantanu Pal Australia	17	448 0.6×	361 0.6×	449 1.2×	244 0.9×	10 0.1×	112	1.2k

Countries citing papers authored by Dongjin Yu

Since Specialization

Citations

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

Fields of papers citing papers by Dongjin Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongjin Yu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Yu, Dongjin, et al.. (2025). Leveraging multi-task learning to fine-tune RoBERTa for self-admitted technical debt identification and classification. Journal of Systems and Software. 231. 112629–112629.

Yu, Dongjin, et al.. (2024). Modeling dynamic spatio-temporal correlations and transitions with time window partitioning for traffic flow prediction. Expert Systems with Applications. 252. 124187–124187. 5 indexed citations

Fukumoto, Fumiyo, et al.. (2024). NFARec: A Negative Feedback-Aware Recommender Model. 935–945. 2 indexed citations

Yu, Dongjin, et al.. (2024). Enhancing structural knowledge in code smell identification: A fusion learning framework combining AST-based metrics with semantic embeddings. Expert Systems with Applications. 263. 125725–125725.

Wang, Dongjing, et al.. (2024). Graph Intention Embedding Neural Network for tag-aware recommendation. Neural Networks. 184. 107062–107062. 8 indexed citations

Yu, Dongjin, et al.. (2023). VulGraB: Graph‐embedding‐based code vulnerability detection with bi‐directional gated graph neural network. Software Practice and Experience. 53(8). 1631–1658. 7 indexed citations

Wang, Dongjing, Xin Zhang, Yuyu Yin, et al.. (2023). Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation. ACM Transactions on Information Systems. 42(1). 1–30. 37 indexed citations

Chen, Jianjiang, et al.. (2023). A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction. Journal of Advanced Transportation. 2023. 1–14. 10 indexed citations

Yang, Yuxuan, et al.. (2023). Siamese learning based on graph differential equation for Next-POI recommendation. Applied Soft Computing. 150. 111086–111086. 14 indexed citations

10.

Huang, Binbin, et al.. (2023). Stateless Q-learning algorithm for service caching in resource constrained edge environment. Journal of Cloud Computing Advances Systems and Applications. 12(1). 3 indexed citations

11.

Yu, Dongjin, et al.. (2023). Iterative framework based on multi-task learning for service recommendation. Journal of Systems and Software. 207. 111873–111873. 1 indexed citations

12.

Zhang, Haiping, et al.. (2023). Multi-Modality Adaptive Feature Fusion Graph Convolutional Network for Skeleton-Based Action Recognition. Sensors. 23(12). 5414–5414. 3 indexed citations

13.

Chen, Jianjiang, et al.. (2022). Graph-Based Attentive Sequential Model With Metadata for Music Recommendation. IEEE Access. 10. 108226–108240. 9 indexed citations

14.

Wang, Dongjing, Xin Zhang, Zhengzhe Xiang, et al.. (2021). Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention. IEEE Transactions on Cybernetics. 52(11). 11893–11905. 11 indexed citations

15.

Wang, Dongjing, Xin Zhang, Yao Wan, et al.. (2021). Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation. IEEE Transactions on Multimedia. 24. 4170–4182. 26 indexed citations

16.

Dibaei, Mahdi, Xi Zheng, Xiwei Xu, et al.. (2021). Investigating the Prospect of Leveraging Blockchain and Machine Learning to Secure Vehicular Networks: A Survey. IEEE Transactions on Intelligent Transportation Systems. 23(2). 683–700. 89 indexed citations

17.

Li, Yong, Yipeng Zhou, Alireza Jolfaei, et al.. (2020). Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing. IEEE Internet of Things Journal. 8(8). 6178–6186. 176 indexed citations

18.

Wang, Dongjing, Xin Zhang, Dongjin Yu, Guandong Xu, & Shuiguang Deng. (2020). CAME: Content- and Context-Aware Music Embedding for Recommendation. IEEE Transactions on Neural Networks and Learning Systems. 32(3). 1375–1388. 52 indexed citations

19.

Wang, Kuan, Jun Wu, Xi Zheng, et al.. (2020). Leveraging Energy Function Virtualization With Game Theory for Fault-Tolerant Smart Grid. IEEE Transactions on Industrial Informatics. 17(1). 678–687. 21 indexed citations

20.

Yin, Yuyu, Honghao Gao, & Dongjin Yu. (2013). Model-Based Data-Intensive Service Abstraction Refinement. 網際網路技術學刊. 14(5). 807–816. 2 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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