Xiangyun Gao

4.7k total citations
134 papers, 3.8k citations indexed

About

Xiangyun Gao is a scholar working on Economics and Econometrics, Statistical and Nonlinear Physics and Environmental Engineering. According to data from OpenAlex, Xiangyun Gao has authored 134 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Economics and Econometrics, 43 papers in Statistical and Nonlinear Physics and 28 papers in Environmental Engineering. Recurrent topics in Xiangyun Gao's work include Complex Systems and Time Series Analysis (54 papers), Market Dynamics and Volatility (52 papers) and Complex Network Analysis Techniques (40 papers). Xiangyun Gao is often cited by papers focused on Complex Systems and Time Series Analysis (54 papers), Market Dynamics and Volatility (52 papers) and Complex Network Analysis Techniques (40 papers). Xiangyun Gao collaborates with scholars based in China, United States and Germany. Xiangyun Gao's co-authors include Haizhong An, Huajiao Li, Weiqiong Zhong, Wei Fang, Xiaoqi Sun, Shupei Huang, Meihui Jiang, Qingru Sun, Xiaoqing Hao and Feng An and has published in prestigious journals such as Nature Communications, Renewable and Sustainable Energy Reviews and PLoS ONE.

In The Last Decade

Xiangyun Gao

132 papers receiving 3.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiangyun Gao China 37 2.2k 846 661 641 390 134 3.8k
Haizhong An China 41 2.6k 1.2× 1.0k 1.2× 715 1.1× 821 1.3× 456 1.2× 156 4.5k
Huajiao Li China 29 1.1k 0.5× 597 0.7× 396 0.6× 296 0.5× 198 0.5× 107 2.5k
Ling Tang China 41 2.1k 1.0× 830 1.0× 84 0.1× 671 1.0× 201 0.5× 137 5.4k
Cuixia Gao China 21 859 0.4× 524 0.6× 93 0.1× 466 0.7× 93 0.2× 44 1.8k
Bo Shen United States 27 1.6k 0.7× 893 1.1× 45 0.1× 938 1.5× 66 0.2× 63 3.6k
Timothy J. Foxon United Kingdom 38 1.2k 0.6× 737 0.9× 56 0.1× 1.2k 1.8× 49 0.1× 95 4.7k
Kaijian He China 25 1.7k 0.8× 356 0.4× 59 0.1× 398 0.6× 160 0.4× 78 2.6k
Ming Zhang China 45 3.5k 1.6× 2.3k 2.7× 19 0.0× 1.2k 1.9× 129 0.3× 155 6.0k
Lianshui Li China 26 2.3k 1.0× 1.1k 1.3× 78 0.1× 644 1.0× 152 0.4× 56 3.4k
Ephraim Bonah Agyekum Russia 39 1.4k 0.6× 691 0.8× 29 0.0× 1.8k 2.8× 67 0.2× 150 4.4k

Countries citing papers authored by Xiangyun Gao

Since Specialization
Citations

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

Fields of papers citing papers by Xiangyun Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiangyun Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Xiangyun Gao. A scholar is included among the top collaborators of Xiangyun Gao 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 Xiangyun Gao. Xiangyun Gao 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.
Zheng, Yingying, et al.. (2025). Impact of charging infrastructure construction on electric vehicle diffusion based on a multi-agent model. iScience. 28(4). 112257–112257. 1 indexed citations
2.
Xi, Xian, Xiangyun Gao, & Weiqiong Zhong. (2025). How risk spillover network structure affects VaR: A study using complex networks and quantile regression. International Review of Economics & Finance. 98. 103956–103956. 1 indexed citations
3.
Zhao, Yiran, Xiangyun Gao, Huiling Zheng, et al.. (2024). Identifying influence pathways of oil price shocks on inflation based on impulse response networks. Energy. 314. 134107–134107. 2 indexed citations
4.
Sun, Xiaotian, et al.. (2024). Dynamic interactions among new energy metals and price adjustment strategies: A cross-industry chain perspective. Energy. 303. 131923–131923. 2 indexed citations
5.
Wu, Tao, Xiangyun Gao, Feng An, et al.. (2024). Predicting multiple observations in complex systems through low-dimensional embeddings. Nature Communications. 15(1). 2242–2242. 15 indexed citations
6.
Wu, Tao, Feng An, Xiangyun Gao, et al.. (2023). Universal window size-dependent transition of correlations in complex systems. Chaos An Interdisciplinary Journal of Nonlinear Science. 33(2). 6 indexed citations
7.
Gao, Xiangyun, et al.. (2022). The impact of structural changes of trade dependence network on cobalt price from the perspective of industrial chain. 资源科学. 44(7). 1344–1357. 4 indexed citations
8.
Wang, Ze, et al.. (2022). Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises. Frontiers in Physics. 9. 2 indexed citations
9.
An, Feng, Sen Wu, Xiangyun Gao, H. Eugene Stanley, & Jianxi Gao. (2021). A quantification method of non-failure cascading spreading in a network of networks. Chaos An Interdisciplinary Journal of Nonlinear Science. 31(12). 123122–123122. 4 indexed citations
10.
Sun, Qingru, et al.. (2021). THE EVOLUTION OF THE ENERGY IMPORT DEPENDENCE NETWORK AND ITS INFLUENCING FACTORS: TAKING COUNTRIES AND REGIONS ALONG THE BELT AND ROAD AS AN EXAMPLE. Journal of Business Economics and Management. 23(1). 105–130. 11 indexed citations
11.
Zheng, Huiling, Xiangyun Gao, Qingru Sun, Xiaodan Han, & Ze Wang. (2020). The impact of regional industrial structure differences on carbon emission differences in China: An evolutionary perspective. Journal of Cleaner Production. 257. 120506–120506. 75 indexed citations
12.
Liu, Siyao, Wei Fang, Xiangyun Gao, et al.. (2019). Long-term memory dynamics of crude oil price spread in non-dollar countries under the influence of exchange rates. Energy. 182. 753–764. 11 indexed citations
13.
Xi, Xian, et al.. (2019). Impact of changes in crude oil trade network patterns on national economy. Energy Economics. 84. 104490–104490. 69 indexed citations
14.
Gao, Xiangyun, et al.. (2019). Network evolution analysis of nickel futures and the spot price linkage effect based on a distributed lag model. International Journal of Modern Physics B. 33(19). 1950206–1950206. 3 indexed citations
15.
Li, Huajiao, et al.. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica A Statistical Mechanics and its Applications. 450. 657–669. 213 indexed citations
16.
Gao, Xiangyun. (2012). RESEARCH ON INFORMATION SERVICE MODEL OF GEOLOGICAL ARCHIVES BASED ON COMPLEX NETWORK. Resources and Industries. 3 indexed citations
17.
Gao, Xiangyun. (2012). STRUCTURAL FEATURES OF GLOBAL GAS TRADING RELATIONSHIP NETWORK BASED ON COMPLEX NETWORK THEORY. Resources and Industries. 5 indexed citations
18.
Gao, Xiangyun. (2012). EVALUATION OF INVESTMENT ENVIRONMENT FOR OIL-GAS RESOURCES IN ASIA-PACIFIC. Resources and Industries. 1 indexed citations
19.
Gao, Xiangyun. (2011). CHINA'S PETROLEUM CONSUMPTION PREDICTION BASED ON TIME-NEURAL NETWORK MODEL. Resources and Industries. 1 indexed citations
20.
An, Haizhong, et al.. (2011). Research on Content Characteristics About Complex Network of Text. Shuju fenxi yu zhishi faxian. 27(1). 69–73. 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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