Yongli Wang

4.4k total citations · 2 hit papers
158 papers, 2.6k citations indexed

About

Yongli Wang is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Yongli Wang has authored 158 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Artificial Intelligence, 31 papers in Computer Networks and Communications and 28 papers in Information Systems. Recurrent topics in Yongli Wang's work include Topic Modeling (19 papers), Sentiment Analysis and Opinion Mining (10 papers) and Advanced Text Analysis Techniques (9 papers). Yongli Wang is often cited by papers focused on Topic Modeling (19 papers), Sentiment Analysis and Opinion Mining (10 papers) and Advanced Text Analysis Techniques (9 papers). Yongli Wang collaborates with scholars based in China, United States and Canada. Yongli Wang's co-authors include Hamed Jelodar, Yanchao Li, Chi Yuan, Xia Feng, Liang Zhao, Rita Orji, Shucheng Huang, Zhou Shao, Ruxin Zhao and Sha Yuan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Physical Review B and Optics Letters.

In The Last Decade

Yongli Wang

135 papers receiving 2.5k citations

Hit Papers

Latent Dirichlet allocation (LDA) and topic modeling: mod... 2018 2026 2020 2023 2018 2020 250 500 750 1000
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. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
    2018 1.1k citations Hamed Jelodar, Yongli Wang et al. profile →
  2. Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
    2020 265 citations Hamed Jelodar, Yongli Wang et al. IEEE Journal of Biomedical and Health Informatics profile →

Peers

Yongli Wang
Xiaohui Yan China
Fred Morstatter United States
Ali Daud Pakistan
Petr Sojka Czechia
Jason Chuang United States
Shimei Pan United States
Chang‐Tien Lu United States
Wenjie Li China
Emily M. Bender United States
Xiaohui Yan China
Yongli Wang
Citations per year, relative to Yongli Wang Yongli Wang (= 1×) peers Xiaohui Yan

Countries citing papers authored by Yongli Wang

Since Specialization
Citations

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

Fields of papers citing papers by Yongli Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yongli Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Yongli Wang. A scholar is included among the top collaborators of Yongli Wang 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 Yongli Wang. Yongli Wang 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.
Zou, Haochen & Yongli Wang. (2025). A novel large language model enhanced joint learning framework for fine-grained sentiment analysis on drug reviews. Neurocomputing. 626. 129589–129589.
2.
Shao, Zhou, et al.. (2024). Scholar's Career Switch from Academia to Industry: Mining and Analysis from AMiner. Big Data Research. 36. 100441–100441. 1 indexed citations
3.
Wang, Yongli, et al.. (2024). Trusted Semi-Supervised Multi-View Classification With Contrastive Learning. IEEE Transactions on Multimedia. 26. 8268–8278. 3 indexed citations
4.
Wang, Yupeng, et al.. (2024). Graph-in-graph discriminative feature enhancement network for fine-grained visual classification. Applied Intelligence. 55(1). 1 indexed citations
5.
6.
Wang, Yongli, et al.. (2023). Reward shaping with hierarchical graph topology. Pattern Recognition. 143. 109746–109746. 5 indexed citations
7.
Zhao, Ruxin, Yongli Wang, Gang Xiao, et al.. (2022). A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer. Applied Intelligence. 52(14). 16775–16798. 21 indexed citations
8.
Fu, Liyong, et al.. (2022). MMatch: Semi-Supervised Discriminative Representation Learning for Multi-View Classification. IEEE Transactions on Circuits and Systems for Video Technology. 32(9). 6425–6436. 27 indexed citations
9.
Zhao, Ruxin, et al.. (2021). A selfish herd optimization algorithm based on the simplex method for clustering analysis. The Journal of Supercomputing. 77(8). 8840–8910. 9 indexed citations
10.
Wang, Yongli, et al.. (2021). Secure Multi-Subinterval Data Aggregation Scheme with Interval Privacy Preservation for Vehicle Sensing Systems. Journal of Circuits Systems and Computers. 30(16).
11.
Jelodar, Hamed, Yongli Wang, Rita Orji, & Shucheng Huang. (2020). Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach. IEEE Journal of Biomedical and Health Informatics. 24(10). 2733–2742. 265 indexed citations breakdown →
12.
Wang, Yongli, Yongjian Wang, Yanchao Li, et al.. (2020). A secure and lightweight privacy-preserving data aggregation scheme for internet of vehicles. Peer-to-Peer Networking and Applications. 13(3). 1002–1013. 28 indexed citations
13.
Wang, Yongli, et al.. (2020). Batch mode active learning via adaptive criteria weights. Applied Intelligence. 51(6). 3475–3489. 4 indexed citations
14.
Zhao, Ruxin, Yongli Wang, Chang Liu, et al.. (2020). Discrete selfish herd optimizer for solving graph coloring problem. Applied Intelligence. 50(5). 1633–1656. 4 indexed citations
15.
Zhao, Ruxin, Yongli Wang, Peng Hu, et al.. (2019). Selfish herds optimization algorithm with orthogonal design and information update for training multi-layer perceptron neural network. Applied Intelligence. 49(6). 2339–2381. 17 indexed citations
16.
Zhao, Ruxin, Yongli Wang, Chang Liu, et al.. (2019). Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem. Soft Computing. 24(10). 7637–7684. 18 indexed citations
17.
Chen, Yiming, et al.. (2018). Design and implementation of vulnerability verification framework based on Docker. SHILAP Revista de lepidopterología. 1 indexed citations
18.
Zhang, Gongxuan, et al.. (2018). A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks. Sensors. 18(5). 1434–1434. 8 indexed citations
19.
Zhang, Gongxuan, et al.. (2016). A New Self-Management Model for Large-Scale Event-Driven Wireless Sensor Networks. IEEE Sensors Journal. 16(20). 7537–7544. 11 indexed citations
20.
Guo, Dong, et al.. (2002). Application of Artificial Neural Network (ANN) Technique to the Formulation Design of BaTiO3 Dielectric Ceramics. Journal of Inorganic Materials. 17(4). 845.

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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