Gang Yan

530 total citations
28 papers, 395 citations indexed

About

Gang Yan is a scholar working on Materials Chemistry, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Gang Yan has authored 28 papers receiving a total of 395 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Materials Chemistry, 8 papers in Artificial Intelligence and 6 papers in Computer Networks and Communications. Recurrent topics in Gang Yan's work include Hydrogen Storage and Materials (8 papers), Graphene research and applications (6 papers) and Boron and Carbon Nanomaterials Research (4 papers). Gang Yan is often cited by papers focused on Hydrogen Storage and Materials (8 papers), Graphene research and applications (6 papers) and Boron and Carbon Nanomaterials Research (4 papers). Gang Yan collaborates with scholars based in China, United States and Japan. Gang Yan's co-authors include Jian Li, Jianlin Yu, Suxin Qian, Zhihong Yang, Yunhui Wang, Lan Bi, Jie Yin, Hao Wang, Qiang Wu and Jun Zhao and has published in prestigious journals such as Applied Physics Letters, Chemical Physics Letters and International Journal of Hydrogen Energy.

In The Last Decade

Gang Yan

25 papers receiving 392 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gang Yan China	12	262	82	54	46	44	28	395
Hanzhang Wang China	8	166 0.6×	73 0.9×	67 1.2×	42 0.9×	40 0.9×	23	366
Dong Ok Kim South Korea	10	171 0.7×	189 2.3×	27 0.5×	29 0.6×	36 0.8×	17	414
Lisheng Huang China	10	198 0.8×	156 1.9×	35 0.6×	50 1.1×	24 0.5×	37	366
Rujun Liu China	9	184 0.7×	131 1.6×	35 0.6×	76 1.7×	42 1.0×	20	359
Weiping Wang China	7	213 0.8×	77 0.9×	56 1.0×	12 0.3×	49 1.1×	35	386
Xingyu Feng China	12	226 0.9×	247 3.0×	28 0.5×	51 1.1×	18 0.4×	30	432
Zhengqiu He China	11	85 0.3×	178 2.2×	124 2.3×	70 1.5×	18 0.4×	31	423
Yong Qin China	10	162 0.6×	25 0.3×	36 0.7×	45 1.0×	13 0.3×	47	403
Parthasarathi Bera India	12	124 0.5×	236 2.9×	23 0.4×	14 0.3×	40 0.9×	38	421

Countries citing papers authored by Gang Yan

Since Specialization

Citations

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

Fields of papers citing papers by Gang Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gang Yan

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

All Works

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20 of 20 papers shown

Wang, Qing, Qingyu Li, Xiao Jin Yang, et al.. (2025). Enhanced hydrogen storage in K and Na decorated DHP-graphene monolayer: DFT and GCMC study. Chemical Physics Letters. 865. 141932–141932. 4 indexed citations

Iwasawa, Yusuke, et al.. (2025). A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics. 10517–10527.

Yan, Gang, Hao Wang, Xu Yuan, & Jian Li. (2024). Enhancing Model Poisoning Attacks to Byzantine-Robust Federated Learning via Critical Learning Periods. 496–512.

Yan, Gang, et al.. (2024). Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates. 434–439. 3 indexed citations

Yan, Gang, et al.. (2024). DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations. Proceedings of the AAAI Conference on Artificial Intelligence. 38(14). 16103–16111. 2 indexed citations

Yan, Gang, Hao Wang, Xu Yuan, & Jian Li. (2024). FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation. 3667–3678. 2 indexed citations

Chen, Wei, et al.. (2023). The performance of Li-modified B-doped phagraphene as a reversible hydrogen storage material in near-environmental conditions. Applied Surface Science. 638. 157950–157950. 14 indexed citations

Yan, Gang, et al.. (2023). DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 10711–10719. 19 indexed citations

Yan, Gang, et al.. (2023). Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach. IEEE/ACM Transactions on Networking. 31(5). 2147–2161. 8 indexed citations

10.

Shan, Feng, et al.. (2023). A Stochastic Algorithm Based on Cable-stayed Bridges Model for Stochastic Optimization in Machine Learning. Journal of Physics Conference Series. 2607(1). 12014–12014. 2 indexed citations

11.

Xu, Yi, Yiming Zhang, Xin Huang, et al.. (2023). Carbon doping of B6N6 monolayer can improve its hydrogen storage performance effectively: A theoretical study. International Journal of Hydrogen Energy. 50. 475–483. 12 indexed citations

12.

Yan, Gang, Hao Wang, Xu Yuan, & Jian Li. (2023). CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. 2898–2907. 17 indexed citations

13.

Yan, Gang, et al.. (2022). Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 7587–7601. 15 indexed citations

14.

Yan, Gang, Hao Wang, & Jian Li. (2022). Seizing Critical Learning Periods in Federated Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 8788–8796. 20 indexed citations

15.

Zhao, Jun, et al.. (2021). Spin-valley coupling and valley splitting in the MoSi2N4/CrCl3 van der Waals heterostructure. Applied Physics Letters. 119(21). 45 indexed citations

16.

Wu, Qiang, Xin Huang, Zhaoshun Meng, et al.. (2021). A density functional theory and grand canonical Monte Carlo simulations study the hydrogen storage on the Li-decorated net-τ. International Journal of Hydrogen Energy. 46(42). 21965–21972. 7 indexed citations

17.

Yin, Jie, et al.. (2020). Boron nitride nanoribbons with single vacancy defects and doped with 3d transition metals: A first-principles study. Materials Today Communications. 26. 101861–101861. 5 indexed citations

18.

Yin, Jie, Zhihong Yang, Lan Bi, et al.. (2019). Electronic and magnetic properties of Fe-doped narrow zigzag boron nitride nanoribbons. Materials Today Communications. 22. 100753–100753. 7 indexed citations

19.

Wang, Yunhui, Qiang Wu, Jianing Mao, et al.. (2019). Lithium and calcium decorated triphenylene-graphdiyne as potential high-capacity hydrogen storage medium: A first-principles prediction. Applied Surface Science. 494. 763–770. 47 indexed citations

20.

Bi, Lan, Jie Yin, Xin Huang, et al.. (2018). Lithium decoration of boron-doped hybrid fullerenes and nanotubes as a novel 3D architecture for enhanced hydrogen storage: A DFT study. International Journal of Hydrogen Energy. 44(5). 2934–2942. 39 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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