Changjun Fan

1.3k total citations · 1 hit paper
38 papers, 755 citations indexed

About

Changjun Fan is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Management Science and Operations Research. According to data from OpenAlex, Changjun Fan has authored 38 papers receiving a total of 755 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 13 papers in Statistical and Nonlinear Physics and 6 papers in Management Science and Operations Research. Recurrent topics in Changjun Fan's work include Advanced Graph Neural Networks (12 papers), Complex Network Analysis Techniques (12 papers) and Organic Light-Emitting Diodes Research (6 papers). Changjun Fan is often cited by papers focused on Advanced Graph Neural Networks (12 papers), Complex Network Analysis Techniques (12 papers) and Organic Light-Emitting Diodes Research (6 papers). Changjun Fan collaborates with scholars based in China, United States and Sweden. Changjun Fan's co-authors include Yizhou Sun, Li Zeng, Yang‐Yu Liu, Muhao Chen, Guangquan Cheng, Cunchao Zhu, Yan Zhang, Zhong Liu, Yong Cai and Guangliang Liu and has published in prestigious journals such as Nature Communications, Analytical Chemistry and ACS Applied Materials & Interfaces.

In The Last Decade

Changjun Fan

35 papers receiving 745 citations

Hit Papers

Finding key players in complex networks through deep rein... 2020 2026 2022 2024 2020 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Finding key players in complex networks through deep reinforcement learning
    2020 243 citations Changjun Fan, Li Zeng et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Changjun Fan China 14 332 238 108 90 64 38 755
Yuchen Zhao China 15 256 0.8× 102 0.4× 119 1.1× 43 0.5× 117 1.8× 67 741
Shuxin Liu China 15 283 0.9× 210 0.9× 153 1.4× 93 1.0× 15 0.2× 84 608
Yuepeng Wang China 16 215 0.6× 68 0.3× 83 0.8× 69 0.8× 22 0.3× 64 796
Kevin S. Xu United States 12 141 0.4× 184 0.8× 43 0.4× 27 0.3× 53 0.8× 29 473
Zeyu Zheng China 15 83 0.3× 79 0.3× 42 0.4× 137 1.5× 144 2.3× 65 953
Yuexia Zhang China 17 113 0.3× 98 0.4× 156 1.4× 356 4.0× 93 1.5× 98 968

Countries citing papers authored by Changjun Fan

Since Specialization
Citations

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

Fields of papers citing papers by Changjun Fan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Changjun Fan

This figure shows the co-authorship network connecting the top 25 collaborators of Changjun Fan. A scholar is included among the top collaborators of Changjun Fan 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 Changjun Fan. Changjun Fan 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.
Li, Hong, Mengqiao Xu, Yu Liu, Xuemei Zhang, & Changjun Fan. (2025). A deep learning dismantling approach for understanding the structural vulnerability of complex networks. Chaos Solitons & Fractals. 194. 116148–116148. 1 indexed citations
2.
Wang, Rui, et al.. (2025). Toward bridging the gap between machine intelligence and machine wisdom: Dilemmas and conjectures. The Innovation. 6(7). 100834–100834. 1 indexed citations
3.
Chen, Chao, et al.. (2025). Finding key edges in complex network through line graph transformation and deep reinforcement learning. Expert Systems with Applications. 289. 128121–128121.
4.
Hu, Zeng, Hao Yan, Kuihua Huang, et al.. (2025). DTIU: A self-supervised grid-enhanced diffusion model for trajectory imputation in unconstrained scenarios. Knowledge-Based Systems. 325. 113848–113848.
5.
Liao, Haoran, Wei Liu, Changjun Fan, et al.. (2024). Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools. Mathematical Biosciences & Engineering. 21(6). 6289–6335. 5 indexed citations
6.
Zheng, Yuhao, et al.. (2024). FGITrans: Cross-City Transformer for Fine-grained Urban Flow Inference. 3415–3423. 2 indexed citations
7.
8.
Fan, Changjun, et al.. (2023). Searching for spin glass ground states through deep reinforcement learning. Nature Communications. 14(1). 725–725. 15 indexed citations
9.
Zhu, Zhengqiu, Bin Chen, Hailiang Chen, et al.. (2022). Strategy evaluation and optimization with an artificial society toward a Pareto optimum. The Innovation. 3(5). 100274–100274. 8 indexed citations
10.
Jin, Lei, et al.. (2022). The Impact of Artificial Intelligence on the Accounting Industry. Advances in Social Science, Education and Humanities Research. 664. 6 indexed citations
11.
Zhu, Cunchao, Muhao Chen, Changjun Fan, Guangquan Cheng, & Yan Zhang. (2021). Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 4732–4740. 153 indexed citations
12.
Zeng, Weixin, Xiang Zhao, Jiuyang Tang, & Changjun Fan. (2021). Reinforced Active Entity Alignment. 2477–2486. 14 indexed citations
13.
Chen, Xuelu, et al.. (2020). Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer. 3227–3238. 20 indexed citations
14.
Fan, Changjun, Li Zeng, Yizhou Sun, & Yang‐Yu Liu. (2020). Finding key players in complex networks through deep reinforcement learning. Nature Machine Intelligence. 2(6). 317–324. 243 indexed citations breakdown →
15.
Fan, Changjun, et al.. (2020). Speciation of thioarsenicals through application of coffee ring effect on gold nanofilm and surface-enhanced Raman spectroscopy. Analytica Chimica Acta. 1106. 88–95. 17 indexed citations
16.
Fan, Changjun, et al.. (2019). Learning to Identify High Betweenness Centrality Nodes from Scratch. 559–568. 41 indexed citations
17.
Fan, Changjun, Guangliang Liu, Yanmin Long, Barry P. Rosen, & Yong Cai. (2018). Thiolation in arsenic metabolism: a chemical perspective. Metallomics. 10(10). 1368–1382. 29 indexed citations
18.
Lei, Yong, Zhen Liu, Changjun Fan, et al.. (2016). Solution-Processed Conducting Polymer/Metal Oxide Charge Generation Layer: Preparation, Electrical Properties, and Charge Generation Mechanism. The Journal of Physical Chemistry C. 121(1). 793–800. 12 indexed citations
19.
Fan, Changjun, et al.. (2015). Phosphorescent hybrid organic-inorganic light emitting devices with solution-processed small molecule emissive layers. Acta Physica Sinica. 64(16). 167801–167801. 1 indexed citations
20.
Chen, Xi, et al.. (2013). Photochromic copolymers containing 3-indolylfulgides/indolylfulgimides: Synthesis and photochemical properties in toluene and as films. Polymer Degradation and Stability. 98(9). 1662–1670. 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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