Sheng-Jun Wang

920 total citations
57 papers, 678 citations indexed

About

Sheng-Jun Wang is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Cognitive Neuroscience. According to data from OpenAlex, Sheng-Jun Wang has authored 57 papers receiving a total of 678 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistical and Nonlinear Physics, 16 papers in Computer Networks and Communications and 14 papers in Cognitive Neuroscience. Recurrent topics in Sheng-Jun Wang's work include Neural dynamics and brain function (14 papers), Nonlinear Dynamics and Pattern Formation (9 papers) and Complex Network Analysis Techniques (9 papers). Sheng-Jun Wang is often cited by papers focused on Neural dynamics and brain function (14 papers), Nonlinear Dynamics and Pattern Formation (9 papers) and Complex Network Analysis Techniques (9 papers). Sheng-Jun Wang collaborates with scholars based in China, Hong Kong and United States. Sheng-Jun Wang's co-authors include Changsong Zhou, Claus C. Hilgetag, Jie Ma, Xinyi Tang, Yuhan Chen, Kai Yin, Dongwei Zhu, Long Zhao, Selvaraj Muthusamy and Ying-Hai Wang and has published in prestigious journals such as Physical review. B, Condensed matter, Scientific Reports and Science Advances.

In The Last Decade

Sheng-Jun Wang

53 papers receiving 662 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sheng-Jun Wang China	15	216	134	119	117	113	57	678
Erez Persi Israel	10	387 1.8×	88 0.7×	199 1.7×	42 0.4×	30 0.3×	19	721
Ikuko N. Motoike Japan	17	357 1.7×	67 0.5×	87 0.7×	46 0.4×	30 0.3×	50	824
Subrata Ghosh India	14	164 0.8×	104 0.8×	17 0.1×	64 0.5×	27 0.2×	67	812
Avner Priel Israel	14	274 1.3×	87 0.6×	51 0.4×	70 0.6×	8 0.1×	24	837
Eric A. Sobie United States	32	2.3k 10.7×	72 0.5×	77 0.6×	85 0.7×	27 0.2×	115	3.4k
Kathryn Hess Switzerland	14	164 0.8×	235 1.8×	35 0.3×	81 0.7×	4 0.0×	64	1.1k
Francisco Torrealdea Spain	15	71 0.3×	298 2.2×	38 0.3×	395 3.4×	30 0.3×	34	1.0k
Yoon Sup Choi South Korea	9	632 2.9×	29 0.2×	19 0.2×	64 0.5×	26 0.2×	12	870
Gregorio Alanis‐Lobato Germany	16	1.0k 4.8×	33 0.2×	53 0.4×	331 2.8×	32 0.3×	33	1.4k
James E. Ferrell United States	8	1.2k 5.4×	35 0.3×	49 0.4×	74 0.6×	14 0.1×	9	1.4k

Countries citing papers authored by Sheng-Jun Wang

Since Specialization

Citations

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

Fields of papers citing papers by Sheng-Jun Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheng-Jun Wang. 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 Sheng-Jun Wang. The network helps show where Sheng-Jun Wang may publish in the future.

Co-authorship network of co-authors of Sheng-Jun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Sheng-Jun Wang. A scholar is included among the top collaborators of Sheng-Jun Wang 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 Sheng-Jun Wang. Sheng-Jun Wang 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

Wang, Sheng-Jun, et al.. (2024). Improving model-free prediction of chaotic dynamics by purifying the incomplete input. AIP Advances. 14(12). 2 indexed citations

Yan, Youfang, et al.. (2023). Predicting chaotic dynamics from incomplete input via reservoir computing with (D+1)-dimension input and output. Physical review. E. 107(5). 54209–54209. 5 indexed citations

Fang, Fei, et al.. (2021). Power law decay of stored pattern stability in sparse Hopfield neural networks. Communications in Theoretical Physics. 73(2). 25601–25601. 1 indexed citations

Muthusamy, Selvaraj, Kanagaraj Rajalakshmi, Dongwei Zhu, et al.. (2020). A novel lysosome targeted fluorophore for H2S sensing: Enhancing the quantitative detection with successive reaction sites. Sensors and Actuators B Chemical. 320. 128433–128433. 55 indexed citations

Xu, Mengxue, Feiting Xie, Xinyi Tang, Tingting Wang, & Sheng-Jun Wang. (2020). Insights into the role of circular RNA in macrophage activation and fibrosis disease. Pharmacological Research. 156. 104777–104777. 28 indexed citations

Xia, Xueli, Wenxin Wang, Kai Yin, & Sheng-Jun Wang. (2020). Interferon regulatory factor 8 governs myeloid cell development. Cytokine & Growth Factor Reviews. 55. 48–57. 15 indexed citations

Wang, Fan & Sheng-Jun Wang. (2019). Effects of Inhibitory Signal on Criticality in Excitatory-Inhibitory Networks*. Communications in Theoretical Physics. 71(6). 746–746. 2 indexed citations

Lü, Wei, et al.. (2019). LncRNAs: The Regulator of Glucose and Lipid Metabolism in Tumor Cells. Frontiers in Oncology. 9. 1099–1099. 34 indexed citations

Yin, Kai, Xinyi Tang, Jie Tian, et al.. (2019). Metformin inhibits the function of granulocytic myeloid-derived suppressor cells in tumor-bearing mice. Biomedicine & Pharmacotherapy. 120. 109458–109458. 53 indexed citations

10.

Wu, Jing, Jiaqi Gu, Li Shen, et al.. (2019). Exosomal MicroRNA-155 Inhibits Enterovirus A71 Infection by Targeting PICALM. International Journal of Biological Sciences. 15(13). 2925–2935. 19 indexed citations

11.

Zhou, Qinfeng, Xinyi Tang, Xinyu Tian, et al.. (2018). LncRNA MALAT1 negatively regulates MDSCs in patients with lung cancer. Journal of Cancer. 9(14). 2436–2442. 58 indexed citations

12.

Wang, Sheng-Jun & Zhou Yang. (2017). Effect of similarity between patterns in associative memory. Physical review. E. 95(1). 12309–12309. 2 indexed citations

13.

Wang, Sheng-Jun, et al.. (2016). Synchronous slowing down in coupled logistic maps via random network topology. Scientific Reports. 6(1). 23448–23448. 4 indexed citations

14.

Wang, Sheng-Jun, et al.. (2015). Biased random walks in the scale-free networks with the disassortative degree correlation. Acta Physica Sinica. 64(2). 28901–28901. 2 indexed citations

15.

Qu, Jing, Sheng-Jun Wang, Marko Jusup, & Zhen Wang. (2015). Effects of random rewiring on the degree correlation of scale-free networks. Scientific Reports. 5(1). 15450–15450. 9 indexed citations

16.

Wang, Sheng-Jun, Zhen Wang, Jin Tao, & Stefano Boccaletti. (2014). Emergence of disassortative mixing from pruning nodes in growing scale-free networks. Scientific Reports. 4(1). 7536–7536. 13 indexed citations

17.

Wang, Sheng-Jun, Xin‐Jian Xu, Zhi-Xi Wu, Zi‐Gang Huang, & Ying-Hai Wang. (2008). Influence of synaptic interaction on firing synchronization and spike death in excitatory neuronal networks. Physical Review E. 78(6). 61906–61906. 15 indexed citations

18.

Wang, Sheng-Jun, et al.. (2007). Response of degree-correlated scale-free networks to stimuli. Physical Review E. 75(4). 46113–46113. 13 indexed citations

19.

Wang, Sheng-Jun, et al.. (2006). Parental Cluster Analysis in Indica Hybrid Rice(Oryza sativa L.) by SSR Analysis. Zuo wu xue bao. 32(10). 1437–1443. 2 indexed citations

20.

Wang, Changsui, et al.. (1991). RESEARCH ON POWDERY CORROSION OF THE SERIALS BELLS FROM CAI HOU TOMB. Science China Chemistry. 34(5). 522–531. 1 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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