Yequan Wang

2.9k total citations · 1 hit paper
26 papers, 1.8k citations indexed

About

Yequan Wang is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yequan Wang has authored 26 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 2 papers in Information Systems and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yequan Wang's work include Topic Modeling (13 papers), Sentiment Analysis and Opinion Mining (6 papers) and Advanced Text Analysis Techniques (6 papers). Yequan Wang is often cited by papers focused on Topic Modeling (13 papers), Sentiment Analysis and Opinion Mining (6 papers) and Advanced Text Analysis Techniques (6 papers). Yequan Wang collaborates with scholars based in China, Singapore and United States. Yequan Wang's co-authors include Xiaoyan Zhu, Minlie Huang, Zhao Li, Aixin Sun, Ying Liu, Jialong Han, Xiaoyan Zhu, Xuying Meng, Jing Li and Yujun Zhang and has published in prestigious journals such as IEEE/ACM Transactions on Networking, Applied Acoustics and Knowledge and Information Systems.

In The Last Decade

Yequan Wang

21 papers receiving 1.7k citations

Hit Papers

Attention-based LSTM for Aspect-level Sentiment Classific... 2016 2026 2019 2022 2016 500 1000 1.5k
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. Attention-based LSTM for Aspect-level Sentiment Classification
    2016 1.5k citations Yequan Wang, Minlie Huang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yequan Wang China 9 1.5k 202 114 79 76 26 1.8k
Rui Xia China 24 1.4k 0.9× 273 1.4× 138 1.2× 150 1.9× 78 1.0× 47 1.7k
Rehab Duwairi Jordan 20 993 0.6× 349 1.7× 145 1.3× 82 1.0× 64 0.8× 65 1.4k
Shahla Nemati Iran 12 894 0.6× 159 0.8× 120 1.1× 161 2.0× 61 0.8× 19 1.2k
Pengfei Liu China 23 2.0k 1.3× 236 1.2× 316 2.8× 60 0.8× 132 1.7× 80 2.3k
Mondher Bouazizi Japan 17 829 0.5× 285 1.4× 181 1.6× 86 1.1× 92 1.2× 73 1.2k
Yunfan Shao China 6 948 0.6× 198 1.0× 247 2.2× 40 0.5× 77 1.0× 10 1.3k
Daling Wang China 16 713 0.5× 119 0.6× 198 1.7× 40 0.5× 53 0.7× 78 911
Dipankar Das India 16 806 0.5× 150 0.7× 193 1.7× 53 0.7× 78 1.0× 124 1.1k

Countries citing papers authored by Yequan Wang

Since Specialization
Citations

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

Fields of papers citing papers by Yequan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yequan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Yequan Wang. A scholar is included among the top collaborators of Yequan 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 Yequan Wang. Yequan 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.
Li, Jianming, Guodong Du, Jing Li, et al.. (2025). Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling. 30866–30887.
2.
Zhou, Yiming, Wu Li, Yifan Lu, et al.. (2025). Reflection on Knowledge Graph for Large Language Models Reasoning. 23840–23857.
3.
Zhou, Yiming, Jing Li, Yequan Wang, et al.. (2025). Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence. 39(23). 24741–24749. 1 indexed citations
4.
Wang, Xinyuan, et al.. (2024). BiPFT: Binary Pre-trained Foundation Transformer with Low-Rank Estimation of Binarization Residual Polynomials. Proceedings of the AAAI Conference on Artificial Intelligence. 38(14). 16094–16102.
5.
Liu, Kang, et al.. (2024). Contrastive Language-knowledge Graph Pre-training. ACM Transactions on Asian and Low-Resource Language Information Processing. 23(4). 1–21. 1 indexed citations
6.
Wang, Yequan, et al.. (2024). Multimodal Reasoning with Multimodal Knowledge Graph. 10767–10782. 10 indexed citations
7.
8.
Meng, Xuying, et al.. (2024). Spectral-Based Graph Neural Networks for Complementary Item Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(8). 8868–8876. 2 indexed citations
9.
Wang, Yequan, et al.. (2023). Rethinking Document-Level Relation Extraction: A Reality Check. 5715–5730. 4 indexed citations
10.
Zhao, Ziwang, et al.. (2023). Knowledgeable Parameter Efficient Tuning Network for Commonsense Question Answering. 9051–9063. 3 indexed citations
11.
Wang, Yequan, et al.. (2023). UaMC: user-augmented conversation recommendation via multi-modal graph learning and context mining. World Wide Web. 26(6). 4109–4129. 4 indexed citations
12.
Wang, Yequan, et al.. (2023). MAMGAN: Multiscale attention metric GAN for monaural speech enhancement in the time domain. Applied Acoustics. 209. 109385–109385. 15 indexed citations
13.
Hu, Lili, et al.. (2023). An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns. Advances in Structural Engineering. 26(8). 1538–1561. 1 indexed citations
14.
Wang, Yequan, et al.. (2022). CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts. 7064–7075. 2 indexed citations
15.
Wang, Yequan, et al.. (2022). Interactive Information Extraction by Semantic Information Graph. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 4100–4106. 4 indexed citations
16.
Meng, Xuying, et al.. (2022). Packet Representation Learning for Traffic Classification. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3546–3554. 9 indexed citations
17.
Meng, Xuying, Yequan Wang, Suhang Wang, Di Yao, & Yujun Zhang. (2021). Interactive Anomaly Detection in Dynamic Communication Networks. IEEE/ACM Transactions on Networking. 29(6). 2602–2615. 4 indexed citations
18.
Lin, Xi, Yequan Wang, Xiaokui Xiao, Zengxiang Li, & Sourav S. Bhowmick. (2019). Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network. 1973–1982. 8 indexed citations
19.
Wang, Yequan, Aixin Sun, Jialong Han, Ying Liu, & Xiaoyan Zhu. (2018). Sentiment Analysis by Capsules. ORCA Online Research @Cardiff (Cardiff University). 1165–1174. 134 indexed citations
20.
Wang, Yequan, Minlie Huang, Xiaoyan Zhu, & Zhao Li. (2016). Attention-based LSTM for Aspect-level Sentiment Classification. 606–615. 1507 indexed citations breakdown →

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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