Fangzhao Wu

8.2k total citations · 3 hit papers
118 papers, 4.2k citations indexed

About

Fangzhao Wu is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fangzhao Wu has authored 118 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Artificial Intelligence, 60 papers in Information Systems and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fangzhao Wu's work include Topic Modeling (67 papers), Recommender Systems and Techniques (45 papers) and Sentiment Analysis and Opinion Mining (33 papers). Fangzhao Wu is often cited by papers focused on Topic Modeling (67 papers), Recommender Systems and Techniques (45 papers) and Sentiment Analysis and Opinion Mining (33 papers). Fangzhao Wu collaborates with scholars based in China, United States and Hong Kong. Fangzhao Wu's co-authors include Yongfeng Huang, Chuhan Wu, Xing Xie, Tao Qi, Lingjuan Lyu, Zhigang Yuan, Sixing Wu, Junxin Liu, Mingxiao An and Zheng Liu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Expert Systems with Applications.

In The Last Decade

Fangzhao Wu

117 papers receiving 4.1k citations

Hit Papers

Communication-efficient federated learning via knowledge ... 2020 2026 2022 2024 2022 2020 2022 100 200 300
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. Communication-efficient federated learning via knowledge distillation
    2022 314 citations Chuhan Wu, Fangzhao Wu et al. profile →
  2. MIND: A Large-scale Dataset for News Recommendation
    2020 238 citations Fangzhao Wu, Chuhan Wu et al. profile →
  3. A federated graph neural network framework for privacy-preserving personalization
    2022 139 citations Chuhan Wu, Fangzhao Wu 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
Fangzhao Wu China 37 3.5k 2.1k 644 316 306 118 4.2k
Le Wu China 31 2.4k 0.7× 2.3k 1.1× 757 1.2× 181 0.6× 371 1.2× 117 3.7k
Yongfeng Zhang United States 32 3.2k 0.9× 2.8k 1.3× 844 1.3× 198 0.6× 206 0.7× 107 4.2k
Zhaochun Ren China 33 3.1k 0.9× 2.2k 1.0× 731 1.1× 124 0.4× 240 0.8× 147 3.9k
Guibing Guo China 26 1.4k 0.4× 2.1k 1.0× 577 0.9× 268 0.8× 468 1.5× 91 2.5k
Fuzheng Zhang China 22 3.6k 1.0× 3.0k 1.4× 852 1.3× 152 0.5× 354 1.2× 52 4.7k
Chenliang Li China 32 3.4k 1.0× 1.5k 0.7× 595 0.9× 213 0.7× 232 0.8× 134 4.5k
Chen Gao China 25 1.5k 0.4× 1.6k 0.8× 463 0.7× 147 0.5× 215 0.7× 95 2.5k
Ting Liu China 31 4.8k 1.4× 1.6k 0.8× 429 0.7× 183 0.6× 311 1.0× 127 5.8k
Prem Melville United States 21 1.7k 0.5× 1.0k 0.5× 531 0.8× 158 0.5× 224 0.7× 40 2.7k

Countries citing papers authored by Fangzhao Wu

Since Specialization
Citations

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

Fields of papers citing papers by Fangzhao Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fangzhao Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Fangzhao Wu. A scholar is included among the top collaborators of Fangzhao 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 Fangzhao Wu. Fangzhao 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.
Xie, Yueqi, et al.. (2025). Benchmarking and Defending against Indirect Prompt Injection Attacks on Large Language Models. 1809–1820. 4 indexed citations
2.
Yi, Jingwei, Rui Ye, Bin Zhu, et al.. (2024). On the Vulnerability of Safety Alignment in Open-Access LLMs. 9236–9260. 2 indexed citations
3.
Yin, Hongzhi, et al.. (2023). Federated Unlearning for On-Device Recommendation. 393–401. 60 indexed citations
4.
Han, Sungwon, Fangzhao Wu, Sundong Kim, et al.. (2023). DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision. 3766–3774. 3 indexed citations
5.
Han, Sungwon, et al.. (2023). FedDefender: Client-Side Attack-Tolerant Federated Learning. 1850–1861. 17 indexed citations
6.
Peng, Wenjun, Jingwei Yi, Fangzhao Wu, et al.. (2023). Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark. 7653–7668. 21 indexed citations
7.
Yi, Jingwei, Fangzhao Wu, Chuhan Wu, et al.. (2022). Effective and Efficient Query-aware Snippet Extraction for Web Search. 3035–3046. 1 indexed citations
8.
Wu, Chuhan, Fangzhao Wu, Xiangnan He, & Yongfeng Huang. (2022). DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning. 2933–2938. 2 indexed citations
9.
Wu, Fangzhao, et al.. (2021). Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2814–2824. 34 indexed citations
10.
Wu, Chuhan, Fangzhao Wu, Tao Qi, & Yongfeng Huang. (2021). Fastformer: Additive Attention is All You Need. arXiv (Cornell University). 1 indexed citations
11.
Qi, Tao, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, & Xing Xie. (2020). Privacy-Preserving News Recommendation Model Learning. 1423–1432. 85 indexed citations
12.
Wu, Chuhan, Fangzhao Wu, Tao Qi, & Yongfeng Huang. (2020). SentiRec: Sentiment Diversity-aware Neural News Recommendation. 44–53. 21 indexed citations
13.
Qi, Tao, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, & Xing Xie. (2020). Privacy-Preserving News Recommendation Model Training via Federated Learning.. arXiv (Cornell University). 6 indexed citations
14.
Wu, Chuhan, Fangzhao Wu, Tao Qi, et al.. (2020). PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision. 1939–1944. 23 indexed citations
15.
16.
Wu, Chuhan, Fangzhao Wu, Suyu Ge, et al.. (2019). Neural News Recommendation with Multi-Head Self-Attention. 6388–6393. 188 indexed citations
17.
An, Mingxiao, Fangzhao Wu, Chuhan Wu, et al.. (2019). Neural News Recommendation with Long- and Short-term User Representations. 336–345. 185 indexed citations
18.
Ma, Dehong, Sujian Li, Fangzhao Wu, Xing Xie, & Houfeng Wang. (2019). Exploring Sequence-to-Sequence Learning in Aspect Term Extraction. 3538–3547. 97 indexed citations
19.
Wu, Chuhan, Fangzhao Wu, Yongfeng Huang, Sixing Wu, & Zhigang Yuan. (2017). THU_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM. International Joint Conference on Natural Language Processing. 47–52. 15 indexed citations
20.
Wu, Fangzhao & Yongfeng Huang. (2016). Sentiment Domain Adaptation with Multiple Sources. 301–310. 63 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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