Mengjia Xu

922 total citations
26 papers, 549 citations indexed

About

Mengjia Xu is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mengjia Xu has authored 26 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistical and Nonlinear Physics, 8 papers in Artificial Intelligence and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mengjia Xu's work include Complex Systems and Time Series Analysis (6 papers), Chaos control and synchronization (5 papers) and Digital Imaging for Blood Diseases (4 papers). Mengjia Xu is often cited by papers focused on Complex Systems and Time Series Analysis (6 papers), Chaos control and synchronization (5 papers) and Digital Imaging for Blood Diseases (4 papers). Mengjia Xu collaborates with scholars based in United States, China and Spain. Mengjia Xu's co-authors include George Em Karniadakis, Pengjian Shang, Ming Dao, Hong Zhao, Sabia Z. Abidi, Dimitrios Papageorgiou, Quanzheng Li, Xiang Li, Mo Zhang and Dimitrios Pantazis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Mengjia Xu

23 papers receiving 527 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mengjia Xu United States 13 181 168 127 72 55 26 549
Joaquim Cezar Felipe Brazil 10 149 0.8× 196 1.2× 15 0.1× 104 1.4× 21 0.4× 44 482
Yiyuan She United States 15 292 1.6× 238 1.4× 19 0.1× 50 0.7× 19 0.3× 35 1.1k
Francisco Escolano Spain 16 230 1.3× 472 2.8× 84 0.7× 28 0.4× 12 0.2× 62 817
Ralf Peeters Netherlands 17 93 0.5× 122 0.7× 115 0.9× 83 1.2× 16 0.3× 118 1.2k
Branimir Reljin Serbia 15 214 1.2× 375 2.2× 19 0.1× 57 0.8× 67 1.2× 111 897
Paulo S. Rodrigues Brazil 11 173 1.0× 130 0.8× 43 0.3× 80 1.1× 14 0.3× 44 370
Leonarda Carnimeo Italy 12 162 0.9× 86 0.5× 99 0.8× 53 0.7× 16 0.3× 46 444
Long Gao China 9 417 2.3× 269 1.6× 64 0.5× 101 1.4× 14 0.3× 22 800
Georges A. Darbellay Czechia 8 238 1.3× 92 0.5× 111 0.9× 37 0.5× 174 3.2× 12 937
Frédéric Bastien Canada 5 436 2.4× 349 2.1× 26 0.2× 34 0.5× 9 0.2× 8 885

Countries citing papers authored by Mengjia Xu

Since Specialization
Citations

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

Fields of papers citing papers by Mengjia Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengjia Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Mengjia Xu. A scholar is included among the top collaborators of Mengjia Xu 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 Mengjia Xu. Mengjia Xu 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.
Belardinelli, Paolo, Elisabeth B. Marsh, Michael Funke, et al.. (2025). Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories. IEEE Journal of Biomedical and Health Informatics. 29(6). 4463–4473.
2.
Abanov, Alexander G., et al.. (2025). Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning. Scientific Reports. 15(1). 6933–6933.
3.
Marsh, Elisabeth B., Michael Funke, John C. Mosher, et al.. (2024). Hyperbolic Graph Embedding of MEG Brain Networks to Study Brain Alterations in Individuals With Subjective Cognitive Decline. IEEE Journal of Biomedical and Health Informatics. 28(12). 7357–7368. 3 indexed citations
4.
5.
Xu, Mengjia, et al.. (2023). TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers. Neural Networks. 172. 106086–106086. 2 indexed citations
6.
7.
Xu, Mengjia, et al.. (2022). DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs. IEEE Transactions on Neural Networks and Learning Systems. 35(1). 985–998. 8 indexed citations
8.
Zhang, Qian, Konstantina Sampani, Mengjia Xu, et al.. (2022). AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images. Translational Vision Science & Technology. 11(8). 7–7. 15 indexed citations
9.
Xu, Mengjia. (2021). Understanding Graph Embedding Methods and Their Applications. SIAM Review. 63(4). 825–853. 108 indexed citations
10.
Xu, Mengjia, et al.. (2021). A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks. IEEE Transactions on Biomedical Engineering. 68(5). 1579–1588. 33 indexed citations
11.
Qiu, Wei, Jiaming Guo, Xiang Li, et al.. (2020). Multi-label Detection and Classification of Red Blood Cells in Microscopic Images. 4257–4263. 12 indexed citations
12.
Zhang, Mo, Xiang Li, Mengjia Xu, & Quanzheng Li. (2020). Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease. IEEE Journal of Biomedical and Health Informatics. 24(11). 3095–3102. 40 indexed citations
13.
Xu, Mengjia, Zhijiang Wang, Haifeng Zhang, et al.. (2020). A new Graph Gaussian embedding method for analyzing the effects of cognitive training. PLoS Computational Biology. 16(9). e1008186–e1008186. 16 indexed citations
14.
Shang, Pengjian, et al.. (2018). Rényi Entropy and Surrogate Data Analysis for Stock Markets. Fluctuation and Noise Letters. 17(4). 1850035–1850035. 2 indexed citations
15.
Xu, Mengjia, et al.. (2017). Generalized sample entropy analysis for traffic signals based on similarity measure. Physica A Statistical Mechanics and its Applications. 474. 1–7. 12 indexed citations
16.
Xu, Mengjia, Pengjian Shang, & Aijing Lin. (2016). Multiscale recurrence quantification analysis of order recurrence plots. Physica A Statistical Mechanics and its Applications. 469. 381–389. 15 indexed citations
17.
Xu, Mengjia, et al.. (2015). An image-enhancement method based on variable-order fractional differential operators. Bio-Medical Materials and Engineering. 26(1_suppl). S1325–33. 14 indexed citations
18.
Xu, Mengjia & Pengjian Shang. (2015). Generalized permutation entropy analysis based on the two-index entropic form Sq,δ. Chaos An Interdisciplinary Journal of Nonlinear Science. 25(5). 53114–53114. 14 indexed citations
19.
Yang, Jinzhu, Wenjun Tan, Shuang Ma, et al.. (2014). Automatic MRI Brain Tissue Extraction Algorithm Based on Three-Dimensional Gray-Scale Transformation Model. Journal of Medical Imaging and Health Informatics. 4(6). 907–911. 3 indexed citations
20.
Jiang, Shidong & Mengjia Xu. (2006). Size dependence of second-order hyperpolarizability of finite periodic chains under Su-Schrieffer-Heeger model. Europhysics Letters (EPL). 76(4). 670–676. 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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