Jun Xia

1.1k total citations · 1 hit paper
29 papers, 491 citations indexed

About

Jun Xia is a scholar working on Artificial Intelligence, Molecular Biology and Global and Planetary Change. According to data from OpenAlex, Jun Xia has authored 29 papers receiving a total of 491 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Molecular Biology and 7 papers in Global and Planetary Change. Recurrent topics in Jun Xia's work include Advanced Graph Neural Networks (8 papers), Hydrology and Watershed Management Studies (5 papers) and Flood Risk Assessment and Management (5 papers). Jun Xia is often cited by papers focused on Advanced Graph Neural Networks (8 papers), Hydrology and Watershed Management Studies (5 papers) and Flood Risk Assessment and Management (5 papers). Jun Xia collaborates with scholars based in China, Australia and Germany. Jun Xia's co-authors include Stan Z. Li, Lirong Wu, Bozhen Hu, Cheng Tan, Siyuan Li, Zhangyang Gao, Yongjie Xu, Sidong Zeng, Yuanqi Du and Yanqiao Zhu and has published in prestigious journals such as Journal of Hydrology, Environmental Research Letters and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Jun Xia

26 papers receiving 488 citations

Hit Papers

align trajectories

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.

SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

2022 147 citations Jun Xia, Lirong Wu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jun Xia China	11	232	93	69	59	58	29	491
Yanxing Hu China	13	137 0.6×	37 0.4×	29 0.4×	11 0.2×	51 0.9×	30	463
Ying Zou China	15	167 0.7×	132 1.4×	74 1.1×	63 1.1×	64 1.1×	55	644
Haytham Elghazel France	12	216 0.9×	60 0.6×	99 1.4×	12 0.2×	45 0.8×	22	413
Yibo Huang China	9	62 0.3×	54 0.6×	121 1.8×	17 0.3×	25 0.4×	47	321
Feiyue Ye China	10	106 0.5×	12 0.1×	122 1.8×	24 0.4×	50 0.9×	55	354
Tao Ji China	13	115 0.5×	47 0.5×	166 2.4×	14 0.2×	20 0.3×	64	622
Yijun Zuo United States	17	118 0.5×	115 1.2×	84 1.2×	24 0.4×	53 0.9×	52	1.4k
Parisa Kordjamshidi United States	13	491 2.1×	44 0.5×	219 3.2×	32 0.5×	63 1.1×	53	702
K. Kiviluoto Finland	7	269 1.2×	29 0.3×	80 1.2×	18 0.3×	26 0.4×	9	512

Countries citing papers authored by Jun Xia

Since Specialization

Citations

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

Fields of papers citing papers by Jun Xia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Xia

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

1.

Wang, Jihong, et al.. (2025). Temporal goal-aware transformer assisted visual reinforcement learning for virtual table tennis agent. The Visual Computer. 41(10). 7535–7549. 2 indexed citations

2.

She, Dunxian, et al.. (2025). A modified PML model considering anthropogenic heat to estimate urban evapotranspiration. Urban Climate. 64. 102632–102632.

3.

Zhang, Yue, Jun Hou, Yuwei Gu, et al.. (2025). Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data. Environmental Management. 75(3). 694–709. 2 indexed citations

4.

Wang, Jinyang, et al.. (2025). VR table tennis techniques learning for humanoid robot via AMP-Former. The Visual Computer. 41(13). 11555–11570.

5.

Xia, Jun, Shaorong Chen, Yue Liu, et al.. (2024). DiscoGNN: A Sample-Efficient Framework for Self-Supervised Graph Representation Learning. 2876–2888. 2 indexed citations

6.

Xu, Yongjie, Zelin Zang, Bozhen Hu, et al.. (2024). Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation. Briefings in Bioinformatics. 26(1).

7.

Yang, Xihong, Cheng Tan, Yue Liu, et al.. (2023). CONVERT: Contrastive Graph Clustering with Reliable Augmentation. 319–327. 29 indexed citations

8.

Liu, Yue, Ke Liang, Jun Xia, et al.. (2023). Reinforcement Graph Clustering with Unknown Cluster Number. 3528–3537. 12 indexed citations

9.

Xia, Jun, Haitao Lin, Yongjie Xu, et al.. (2023). GNN Cleaner: Label Cleaner for Graph Structured Data. IEEE Transactions on Knowledge and Data Engineering. 1–12. 14 indexed citations

10.

Xia, Jun, Yanqiao Zhu, Yuanqi Du, & Stan Z. Li. (2023). A Systematic Survey of Chemical Pre-trained Models. 6787–6795. 28 indexed citations

11.

Xu, Yongjie, et al.. (2023). Structure-preserving visualization for single-cell RNA-Seq profiles using deep manifold transformation with batch-correction. Communications Biology. 6(1). 369–369. 6 indexed citations

12.

Tan, Cheng, Zhangyang Gao, Lirong Wu, et al.. (2023). Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning. 18770–18782. 81 indexed citations

13.

Tan, Cheng, Zhangyang Gao, Jun Xia, Bozhen Hu, & Stan Z. Li. (2023). Global-Context Aware Generative Protein Design. 1–5. 3 indexed citations

14.

Zeng, Sidong, et al.. (2023). The cause and inspiration of the nonlinear impact of the Three Gorges Dam operation on stage-discharge relation in downstream channels. Journal of Hydrology. 624. 129964–129964. 3 indexed citations

15.

Chen, Kai, Xuelian Zhu, Qi Wang, et al.. (2022). Fbxo45 facilitates pancreatic carcinoma progression by targeting USP49 for ubiquitination and degradation. Cell Death and Disease. 13(3). 231–231. 42 indexed citations

16.

Wu, Lirong, Haitao Lin, Jun Xia, Cheng Tan, & Stan Z. Li. (2022). Multi-level disentanglement graph neural network. Neural Computing and Applications. 34(11). 9087–9101. 9 indexed citations

17.

Cao, Tong, Yue Cui, Yingying Wang, et al.. (2022). CACNA1C-AS2 inhibits cell proliferation and suppresses cell migration and invasion via targeting FBXO45 and PI3K/AKT/mTOR pathways in glioma. APOPTOSIS. 27(11-12). 979–991. 14 indexed citations

18.

Gao, Junjie, Dandan Yang, Hua Huang, et al.. (2022). The role of Fbxo5 in the development of human malignant tumors.. PubMed. 12(4). 1456–1464. 7 indexed citations

19.

Zeng, Sidong, Jianwei Liu, Zhengyang Tang, et al.. (2022). Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin. Water. 14(11). 1692–1692. 14 indexed citations

20.

Xia, Jun, et al.. (2013). Fault diagnosis of hydraulic system based on improved BP neural network technology. 21. 137–140. 3 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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