Ling Wu

816 total citations · 1 hit paper
27 papers, 535 citations indexed

About

Ling Wu is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Ling Wu has authored 27 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Statistical and Nonlinear Physics, 11 papers in Artificial Intelligence and 4 papers in Aerospace Engineering. Recurrent topics in Ling Wu's work include Complex Network Analysis Techniques (11 papers), Opinion Dynamics and Social Influence (6 papers) and Advanced Graph Neural Networks (5 papers). Ling Wu is often cited by papers focused on Complex Network Analysis Techniques (11 papers), Opinion Dynamics and Social Influence (6 papers) and Advanced Graph Neural Networks (5 papers). Ling Wu collaborates with scholars based in China, United Kingdom and Australia. Ling Wu's co-authors include Chi‐Hua Chen, Zhang Qishan, K. Shankar, Yiwei Liu, Kun Guo, Fangying Song, Feng-Jang Hwang, Deqin Wang, Yuzhong Chen and Jinhua Zhang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Ling Wu

24 papers receiving 511 citations

Hit Papers

A deep generative multiscale topology optimization framew... 2025 2026 2025 5 10 15
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. A deep generative multiscale topology optimization framework considering manufacturing defects and parametrical uncertainties
    2025 18 citations Lei Wang, Zeshang Li et al. Computer Methods in Applied Mechanics and Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ling Wu China 9 161 130 106 95 68 27 535
Rui Tang China 15 328 2.0× 11 0.1× 52 0.5× 79 0.8× 68 1.0× 44 675
Dawei Feng China 12 434 2.7× 15 0.1× 180 1.7× 162 1.7× 275 4.0× 64 1.1k
Xiangrui Cai China 12 431 2.7× 12 0.1× 35 0.3× 152 1.6× 29 0.4× 36 729
Farhan Amin South Korea 18 242 1.5× 44 0.3× 87 0.8× 190 2.0× 302 4.4× 72 852
Yonghong Yu China 16 370 2.3× 29 0.2× 27 0.3× 216 2.3× 61 0.9× 49 776
Faliang Huang China 13 396 2.5× 44 0.3× 24 0.2× 152 1.6× 58 0.9× 50 702
Ala’ Abdulmajid Eshmawi Saudi Arabia 14 211 1.3× 17 0.1× 69 0.7× 83 0.9× 132 1.9× 38 585
Mario Manzo Italy 9 302 1.9× 87 0.7× 20 0.2× 126 1.3× 39 0.6× 33 555
Md. Nasim Akhtar Bangladesh 11 114 0.7× 70 0.5× 49 0.5× 73 0.8× 36 0.5× 31 475

Countries citing papers authored by Ling Wu

Since Specialization
Citations

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

Fields of papers citing papers by Ling Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Wu. A scholar is included among the top collaborators of Ling Wu 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 Ling Wu. Ling Wu 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.
Wu, Ling, et al.. (2025). The process and motivations of individual values internalization: a qualitative study. BMC Psychology. 13(1). 1036–1036.
2.
Wu, Ling, et al.. (2025). Linear-time attributed graph clustering via collaborative learning of adaptive anchors. Neurocomputing. 665. 132191–132191.
3.
Wang, Lei, et al.. (2025). A deep generative multiscale topology optimization framework considering manufacturing defects and parametrical uncertainties. Computer Methods in Applied Mechanics and Engineering. 437. 117778–117778. 18 indexed citations breakdown →
4.
Guo, Kun, et al.. (2022). Network Embedding Based on Biased Random Walk for Community Detection in Attributed Networks. IEEE Transactions on Computational Social Systems. 10(5). 2279–2290. 5 indexed citations
5.
Zhang, Jinhua, et al.. (2022). Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy. Entropy. 24(2). 293–293. 34 indexed citations
6.
Guo, Kun, et al.. (2022). Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder. IEEE Transactions on Big Data. 9(2). 569–583. 4 indexed citations
7.
Guo, Kun, et al.. (2022). Network representation learning based on community-aware and adaptive random walk for overlapping community detection. Applied Intelligence. 52(9). 9919–9937. 14 indexed citations
8.
Lin, Zhibin, et al.. (2021). Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach. Expert Systems with Applications. 174. 114771–114771. 24 indexed citations
9.
Wang, Junxiang, et al.. (2021). Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning. Mobile Information Systems. 2021. 1–7. 5 indexed citations
10.
Liu, Cheng‐Yang, et al.. (2021). Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems. Mathematical Problems in Engineering. 2021. 1–12.
11.
Qishan, Zhang, et al.. (2020). Solving MDVRP with Grey Delivery Time based on Improved Quantum Evolutionary Algorithm. 32(3). 110. 4 indexed citations
12.
Wu, Ling, et al.. (2020). An Integrated Model Combining Grey Methods and Neural Networks and its Application to Burst Topic Tendency Prediction. DMU Open Research Archive (De Montfort University). 32(4). 52. 1 indexed citations
13.
Shankar, K., et al.. (2020). Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification. IEEE Access. 8. 118164–118173. 174 indexed citations
14.
Wu, Ling, Zhang Qishan, Chi‐Hua Chen, Kun Guo, & Deqin Wang. (2020). Deep Learning Techniques for Community Detection in Social Networks. IEEE Access. 8. 96016–96026. 61 indexed citations
15.
Chen, Chi‐Hua, Fangying Song, Feng-Jang Hwang, & Ling Wu. (2019). A probability density function generator based on neural networks. Physica A Statistical Mechanics and its Applications. 541. 123344–123344. 67 indexed citations
16.
17.
Wu, Ling, Chi‐Hua Chen, & Zhang Qishan. (2019). A Mobile Positioning Method Based on Deep Learning Techniques. Electronics. 8(1). 59–59. 55 indexed citations
18.
Zuo, Cili, et al.. (2017). Stochastic Fractal Based Multiobjective Fruit Fly Optimization. International Journal of Applied Mathematics and Computer Science. 27(2). 417–433. 1 indexed citations
19.
20.
Wu, Ling, et al.. (2008). Meta-level control of the anytime algorithm for the dynamic weapon-target allocation problem. Journal of Tsinghua University(Science and Technology). 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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