Xiangli Yang

1.1k total citations · 2 hit papers
7 papers, 670 citations indexed

About

Xiangli Yang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mathematics. According to data from OpenAlex, Xiangli Yang has authored 7 papers receiving a total of 670 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Computational Mathematics. Recurrent topics in Xiangli Yang's work include Text and Document Classification Technologies (3 papers), Tensor decomposition and applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Xiangli Yang is often cited by papers focused on Text and Document Classification Technologies (3 papers), Tensor decomposition and applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Xiangli Yang collaborates with scholars based in China, Hong Kong and Malaysia. Xiangli Yang's co-authors include Zixing Song, Irwin King, Zenglin Xu, Laurence T. Yang, Jian Liang, Kun Bai, Jieming Yang, Bing Bai, Debin Liu and Chaojun Zhang and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering and Neural Networks.

In The Last Decade

Xiangli Yang

6 papers receiving 653 citations

Hit Papers

A Survey on Deep Semi-Supervised Learning 2022 2026 2023 2024 2022 2022 100 200 300 400
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 Survey on Deep Semi-Supervised Learning
    2022 462 citations Xiangli Yang, Zixing Song et al. IEEE Transactions on Knowledge and Data Engineering profile →
  2. Graph-Based Semi-Supervised Learning: A Comprehensive Review
    2022 193 citations Zixing Song, Xiangli Yang et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers

Xiangli Yang
Mahmut Kaya Türkiye
Simon Vandenhende Switzerland
Wouter Van Gansbeke Belgium
Zonghai Zhu China
Zixing Song Hong Kong
Zhilong Zhang China
Yooju Shin South Korea
Yunbo Rao China
Nikolay Nagornov Russia
Renjie Song China
Mahmut Kaya Türkiye
Xiangli Yang
Citations per year, relative to Xiangli Yang Xiangli Yang (= 1×) peers Mahmut Kaya

Countries citing papers authored by Xiangli Yang

Since Specialization
Citations

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

Fields of papers citing papers by Xiangli Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiangli Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Xiangli Yang. A scholar is included among the top collaborators of Xiangli Yang 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 Xiangli Yang. Xiangli Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
1.
Zhang, Chaojun, et al.. (2025). Multi-site brain disease identification based on tensor decomposition and personalized federated learning. Neural Networks. 193. 107987–107987.
2.
Yang, Xiangli, et al.. (2024). Temporal Knowledge Extrapolation Based on Fine-Grained Tensor Graph Attention Network for Responsible AI. IEEE Transactions on Artificial Intelligence. 6(2). 448–457. 1 indexed citations
3.
Liu, Debin, et al.. (2023). An Efficient Tensor-Based Transformer for Industrial Internet of Things. IEEE Transactions on Network Science and Engineering. 11(3). 2574–2585. 2 indexed citations
4.
Yang, Xiangli, Zixing Song, Irwin King, & Zenglin Xu. (2022). A Survey on Deep Semi-Supervised Learning. IEEE Transactions on Knowledge and Data Engineering. 35(9). 8934–8954. 462 indexed citations breakdown →
5.
Yang, Xiangli, Jian Liang, Bing Bai, et al.. (2022). ACE: A Coarse-to-Fine Learning Framework for Reliable Representation Learning Against Label Noise. 2022 International Joint Conference on Neural Networks (IJCNN). 6. 1–8. 1 indexed citations
6.
Song, Zixing, Xiangli Yang, Zenglin Xu, & Irwin King. (2022). Graph-Based Semi-Supervised Learning: A Comprehensive Review. IEEE Transactions on Neural Networks and Learning Systems. 34(11). 8174–8194. 193 indexed citations breakdown →
7.
Song, Zixing, Xiangli Yang, Zenglin Xu, & Irwin King. (2021). Graph-based Semi-supervised Learning: A Comprehensive Review. arXiv (Cornell University). 11 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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