Dayong Ye

2.1k total citations
75 papers, 1.3k citations indexed

About

Dayong Ye is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Dayong Ye has authored 75 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 30 papers in Computer Networks and Communications and 20 papers in Information Systems. Recurrent topics in Dayong Ye's work include Privacy-Preserving Technologies in Data (23 papers), Adversarial Robustness in Machine Learning (12 papers) and Service-Oriented Architecture and Web Services (10 papers). Dayong Ye is often cited by papers focused on Privacy-Preserving Technologies in Data (23 papers), Adversarial Robustness in Machine Learning (12 papers) and Service-Oriented Architecture and Web Services (10 papers). Dayong Ye collaborates with scholars based in Australia, Macao and China. Dayong Ye's co-authors include Minjie Zhang, Wanlei Zhou, Tianqing Zhu, Yan Kong, Danny Sutanto, Yun Yang, Yanchun Wang, Qiang He, Athanasios V. Vasilakos and Philip S. Yu and has published in prestigious journals such as IEEE Transactions on Power Systems, Expert Systems with Applications and IEEE Access.

In The Last Decade

Dayong Ye

70 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dayong Ye Australia	20	560	543	346	238	151	75	1.3k
Muneeb Ul Hassan Australia	10	430 0.8×	590 1.1×	629 1.8×	271 1.1×	175 1.2×	19	1.4k
Wajid Rafique China	21	716 1.3×	458 0.8×	407 1.2×	197 0.8×	77 0.5×	39	1.4k
Xiaoxian Yang China	17	475 0.8×	334 0.6×	336 1.0×	249 1.0×	88 0.6×	43	1.2k
Nawab Muhammad Faseeh Qureshi South Korea	22	545 1.0×	382 0.7×	343 1.0×	295 1.2×	80 0.5×	68	1.2k
Niranjan Suri United States	23	1.2k 2.2×	541 1.0×	496 1.4×	250 1.1×	133 0.9×	180	2.0k
Ole‐Christoffer Granmo Norway	22	560 1.0×	775 1.4×	117 0.3×	206 0.9×	84 0.6×	134	1.4k
Alberto Sillitti Italy	26	459 0.8×	496 0.9×	1.1k 3.0×	211 0.9×	122 0.8×	116	1.8k
Chang Xu China	23	542 1.0×	858 1.6×	684 2.0×	248 1.0×	91 0.6×	97	1.5k
Dinesh Verma United States	22	1.3k 2.2×	535 1.0×	381 1.1×	384 1.6×	79 0.5×	126	1.8k
Hejiao Huang China	22	893 1.6×	320 0.6×	575 1.7×	455 1.9×	71 0.5×	122	1.6k

Countries citing papers authored by Dayong Ye

Since Specialization

Citations

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

Fields of papers citing papers by Dayong Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dayong Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Dayong Ye. A scholar is included among the top collaborators of Dayong Ye 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 Dayong Ye. Dayong Ye 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

Ye, Dayong, et al.. (2025). The evolution of cooperation in continuous dilemmas via multi-agent reinforcement learning. Knowledge-Based Systems. 315. 113153–113153. 1 indexed citations

Ye, Dayong, Tianqing Zhu, Derui Wang, et al.. (2025). Reinforcement Unlearning.

Ye, Dayong, et al.. (2025). Model Inversion Attack Against Transfer Learning: Inverting a Model Without Querying It. IEEE Transactions on Dependable and Secure Computing. 22(4). 4472–4487.

Zhu, Tianqing, et al.. (2025). Federated Unlearning With Reinforcement Learning: Adaptive Privacy Preservation for Clients. Journal of Information Security and Applications. 93. 104164–104164.

Ye, Dayong, et al.. (2024). A GNN-based teacher–student framework with multi-advice. Expert Systems with Applications. 250. 123887–123887. 2 indexed citations

Ye, Dayong, et al.. (2024). A federated advisory teacher–student framework with simultaneous learning agents. Knowledge-Based Systems. 305. 112637–112637. 1 indexed citations

Shen, Sheng, Dayong Ye, Tianqing Zhu, & Wanlei Zhou. (2024). Privacy preservation in deep reinforcement learning: A training perspective. Knowledge-Based Systems. 304. 112558–112558. 2 indexed citations

Huo, Huan, Xiaoyu Ding, Sheng Shen, et al.. (2024). Accelerating Learning with AI: Improving Students’ Capability to Receive and Build Automated Feedback for Programming Courses. 1–9.

Wang, Minghao, et al.. (2023). Blockchain-Empowered Multiagent Systems: Advancing IoT Security and Transaction Efficiency. IEEE Internet of Things Journal. 11(7). 11217–11231. 1 indexed citations

10.

Ye, Dayong, et al.. (2023). Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation. Information. 14(11). 597–597. 2 indexed citations

11.

Ye, Dayong, et al.. (2022). Time-optimal and privacy preserving route planning for carpool policy. World Wide Web. 25(3). 1151–1168. 7 indexed citations

12.

Zhu, Tianqing, Dayong Ye, Shuai Zhou, Bo Liu, & Wanlei Zhou. (2022). Label-Only Model Inversion Attacks: Attack With the Least Information. IEEE Transactions on Information Forensics and Security. 18. 991–1005. 31 indexed citations

13.

Zhu, Tianqing, et al.. (2021). Prioritized Experience Replay based on Multi-armed Bandit. Expert Systems with Applications. 189. 116023–116023. 19 indexed citations

14.

Ye, Dayong, Tianqing Zhu, Sheng Shen, Wanlei Zhou, & Philip S. Yu. (2020). Differentially Private Multi-Agent Planning for Logistic-like Problems. IEEE Transactions on Dependable and Secure Computing. 1–1. 15 indexed citations

15.

Ye, Dayong, Minjie Zhang, & Danny Sutanto. (2013). Self-Adaptation-Based Dynamic Coalition Formation in a Distributed Agent Network: A Mechanism and a Brief Survey. IEEE Transactions on Parallel and Distributed Systems. 24(5). 1042–1051. 44 indexed citations

16.

Ye, Dayong, Minjie Zhang, & Danny Sutanto. (2012). Cloning, resource exchange and relation adaptation: a self-organising multi-agent framework. Adaptive Agents and Multi-Agents Systems. 1431–1432. 1 indexed citations

17.

Ye, Dayong, Minjie Zhang, & Danny Sutanto. (2012). Integrating self-organisation into dynamic coalition formation. Adaptive Agents and Multi-Agents Systems. 1253–1254. 2 indexed citations

18.

Sutanto, Danny, Dayong Ye, & Minjie Zhang. (2011). Design of an Intelligent Self-Healing Smart Grid using a Hybrid Multi-Agent Framework. Journal of Electronic Science and Technology. 9(1). 17–22. 1 indexed citations

19.

Ye, Dayong, Minjie Zhang, & Danny Sutanto. (2010). Self-organisation in an agent network via learning. Adaptive Agents and Multi-Agents Systems. 1495–1496. 1 indexed citations

20.

Ye, Zhen, et al.. (2009). DDoS Defense Using TCP_IP Header Analysis and Proactive Tests. 548–552. 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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