Weichao Ding

730 total citations
36 papers, 474 citations indexed

About

Weichao Ding is a scholar working on Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Weichao Ding has authored 36 papers receiving a total of 474 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Networks and Communications, 10 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Weichao Ding's work include IoT and Edge/Fog Computing (12 papers), Cloud Computing and Resource Management (7 papers) and Age of Information Optimization (5 papers). Weichao Ding is often cited by papers focused on IoT and Edge/Fog Computing (12 papers), Cloud Computing and Resource Management (7 papers) and Age of Information Optimization (5 papers). Weichao Ding collaborates with scholars based in China, Chile and United Kingdom. Weichao Ding's co-authors include Fei Luo, Chunhua Gu, Haifeng Lu, Xinping Liu, Zhe Wang, Jing Zhang, Shuai Zheng, Chunhua Gu, Liangxiu Han and Yaohui Chang and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and IEEE Access.

In The Last Decade

Weichao Ding

30 papers receiving 453 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Weichao Ding China 11 337 186 113 86 77 36 474
Fei Luo China 11 353 1.0× 209 1.1× 75 0.7× 92 1.1× 88 1.1× 53 500
Thinh Quang Dinh Singapore 5 377 1.1× 128 0.7× 71 0.6× 174 2.0× 81 1.1× 9 456
Paulo A. L. Rêgo Brazil 11 320 0.9× 230 1.2× 76 0.7× 145 1.7× 84 1.1× 58 460
Shihong Hu China 10 474 1.4× 213 1.1× 66 0.6× 145 1.7× 85 1.1× 17 594
Mian Guo China 11 456 1.4× 194 1.0× 93 0.8× 183 2.1× 54 0.7× 33 525
Colin Funai United States 5 333 1.0× 137 0.7× 101 0.9× 123 1.4× 49 0.6× 5 418
Arsham Farshad United Kingdom 11 426 1.3× 108 0.6× 119 1.1× 207 2.4× 56 0.7× 18 555
Xiantao Jiang China 7 184 0.5× 99 0.5× 162 1.4× 120 1.4× 80 1.0× 27 405
Peng Lin China 13 410 1.2× 113 0.6× 77 0.7× 206 2.4× 68 0.9× 42 541
Weifeng Lu China 10 264 0.8× 122 0.7× 42 0.4× 136 1.6× 75 1.0× 41 375

Countries citing papers authored by Weichao Ding

Since Specialization
Citations

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

Fields of papers citing papers by Weichao Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weichao Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Weichao Ding. A scholar is included among the top collaborators of Weichao Ding 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 Weichao Ding. Weichao Ding 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.
Luo, Fei, et al.. (2025). A Novel Deep Knowledge Tracing Model with Problem Complexity and State Stability. International Journal of Artificial Intelligence in Education. 35(5). 3270–3293.
2.
Ding, Weichao, et al.. (2025). Multi-modal self-supervised contrastive representation learning for three-dimensional point cloud understanding. Engineering Applications of Artificial Intelligence. 160. 111919–111919.
3.
Ding, Weichao, et al.. (2025). Chemical reaction-inspired dual-population co-evolutionary algorithm for many-objective optimization. Expert Systems with Applications. 289. 128257–128257.
4.
Luo, Fei, et al.. (2025). Bidirectional domain transfer knowledge distillation for catastrophic forgetting in federated learning with heterogeneous data. Knowledge-Based Systems. 311. 113008–113008. 1 indexed citations
5.
Luo, Fei, et al.. (2025). A novel multimodel medical image fusion framework with edge enhancement and cross-scale transformer. Scientific Reports. 15(1). 11657–11657. 3 indexed citations
6.
Ding, Weichao, et al.. (2024). Deep discriminative feature learning based on classification-enhanced neural networks for visual process monitoring. Journal of the Taiwan Institute of Chemical Engineers. 156. 105384–105384. 5 indexed citations
7.
Luo, Fei, et al.. (2024). A review on multi-focus image fusion using deep learning. Neurocomputing. 618. 129125–129125. 4 indexed citations
8.
Zheng, Jiaming, et al.. (2024). Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis. Applied Sciences. 14(22). 10309–10309.
9.
Luo, Fei, et al.. (2024). MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation. Multimedia Tools and Applications. 84(12). 10877–10898.
10.
Wang, Tianqi, et al.. (2024). Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics. Communications Biology. 7(1). 1351–1351. 4 indexed citations
11.
Wang, Zhe, et al.. (2024). Discriminative sparse subspace learning with manifold regularization. Expert Systems with Applications. 249. 123831–123831. 6 indexed citations
12.
Zhang, Jing, et al.. (2024). Object aroused emotion analysis network for image sentiment analysis. Knowledge-Based Systems. 286. 111429–111429. 4 indexed citations
13.
Ding, Weichao, et al.. (2023). M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing. Processes. 11(4). 1053–1053. 7 indexed citations
14.
Ding, Weichao, et al.. (2020). Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers. Future Generation Computer Systems. 111. 254–270. 34 indexed citations
15.
Gu, Chunhua, et al.. (2019). Task Scheduling and Resource Allocation Based on Ant-Colony Optimization and Deep Reinforcement Learning. 網際網路技術學刊. 20(5). 1463–1475. 14 indexed citations
16.
Lu, Haifeng, Chunhua Gu, Fei Luo, Weichao Ding, & Xinping Liu. (2019). Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Generation Computer Systems. 102. 847–861. 162 indexed citations
17.
Ding, Weichao, Fei Luo, Chunhua Gu, & Haifeng Lu. (2019). QARPF: A QoS-Aware Active Resource Provisioning Framework Based on OpenStack. 99. 1568–1576. 1 indexed citations
18.
Gu, Chunhua, Fei Luo, LI Ya, & Weichao Ding. (2018). Dynamic Access Control Model Based on FAHP in Cloud Environment. 1938–1943. 1 indexed citations
19.
Luo, Fei, Ye Yuan, Weichao Ding, & Haifeng Lu. (2018). An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud Computing. 1–5. 15 indexed citations
20.
Ding, Weichao, et al.. (2016). DFA-VMP: An efficient and secure virtual machine placement strategy under cloud environment. Peer-to-Peer Networking and Applications. 11(2). 318–333. 27 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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