Menglin Yang

51 Prospect Street
New Haven, CT 06511-8937
Department of Computer Science, Yale University

Google Scholar:


Background. I am currently a Postdoctoral Associate at Yale University, working with Prof. Rex Ying. At Yale University, I received the Tony Massini Postdoctoral Fellowships in Data Science. Prior to that, I obtained my Ph.D. from The Chinese University of Hong Kong, supervised by Prof. Irwin King.

Research Interests. My research is primarily focused on exploring the connections between embedding space, especially the hyperbolic space, and the implicit geometries underlying various types of data. Recently, I have been concentrating on several key areas, including hyperbolic machine learning, Transformers, LLM, recommender systems, and AI for Scientific Discovery (AI4SCI). Please feel free to reach out to me if you have anything you would like to discuss.

👉 New papers, blogs, and books on the hyperbolic representation and deep learning topic are shared in the following slack channel and GitHub repo:

🔥 Welcome to join and share more on this fascinating research.

Recent Works


  1. [KDD 2024] Hypfromer: Exploring Efficient Transformer in Hyperbolic space [paper]
    Menglin Yang, Harshit Verma, Delvin Ce Zhang, Jiahong Liu, Irwin King, Rex Ying


  1. [KDD 2023] κHGCN:Tree-likeness modeling via continuous and discrete curvature [paper]
    Menglin Yang, Min Zhou, Lujia Pan, Irwin King

  2. [ICML 2023] Hierarchical Learning in Hyperbolic Space: Revisit and Beyond [paper]
    Menglin Yang, Min Zhou, Rex Ying, Yankai Chen, Irwin King

  3. [SIGIR 2023] WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering [paper]
    Yankai Chen, Yifei Zhang, Menglin Yang, Zixing Song, Chen Ma, Irwin King


  1. [TKDE 2022] Hyperbolic Temporal Network Embedding [paper][code]
    Menglin Yang, Min Zhou, Hui Xiong, Iriwn King

  2. [SIGKDD 2022] HICF: Hyperbolic Informative Collaborative Filtering (acceptance rate: 15.0%)[pdf][code]
    Menglin Yang, Zhihao Li, Min Zhou, Jiahong Liu, Irwin King

  3. [Survey] Hyperbolic Graph Neural Networks: A Review of Methods and Application [pdf][GitHub]
    Menglin Yang, Min Zhou, Zhihao Li, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King

  4. [WWW 2022] HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization (acceptance rate: 17.7%) [pdf][code]
    Menglin Yang, Min Zhou, Jiahong Liu, Defu Lian, Irwin King

  5. [WSDM 2022] Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation (acceptance rate: 20.23%) [pdf][code]
    Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jian Hao, Irwin King

  6. [ICDE 2022] Discovering Representative Attribute-stars via Minimum Description Length.[pdf]
    Jiahong Liu, Philippe Fournier-Viger, Min Zhou, Lujia Pan, Menglin Yang

  7. [SIGIR 2022] BSAL: A Framework of Bi-component Structural and Attribute Learning for Link Prediction. [pdf] [code]
    Bisheng Li*, Min Zhou*, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang
    *The first two authors have equal contribution

  8. [GLB workshop@WWW 2022] TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction [pdf] [code]
    Min Zhou, Bisheng Li, Menglin Yang, Lujia Pan

Before 2022

  1. [SIGKDD 2021] Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space (acceptance rate: 15.4%) [pdf] [code]
    Menglin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang, Irwin King

  2. [ICDM 2020] FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding (acceptance rate: 9.8%) [pdf] [code]
    Menglin Yang, Ziqiao Meng, Irwin King

  3. [2nd SSL workshop@NeurIPS 2021] Enhancing Hyperbolic Graph Embeddings via Contrastive Learning [pdf]
    Jiahong Liu*, Menglin Yang*, Min Zhou, Shanshan Feng, Philippe Fournier-Viger
    *The first two authors have equal contribution

Talks, Tutorials and Workshops

  1. Text-Attributed Graph Representation Learning: Methods and Applications
    Delvin Zhang, Menglin Yang, Rex Ying and Hady Lauw, WWW 2024.
  2. Geometric Graph Learning: Methods and Applications
    @2023 Long Feng Science Forum by CUHKSZ, August 2023
  3. Hyperbolic Graph Neural Networks: A Tutorial on Methods and Applications
    @KDD2023, August 2023
  4. Geometric Graph Learning: Methods Applications and Opportunities
    @SUSTech Global Computer Scientist Forum. January 7, 2023
  5. Hyperbolic Representation Learning: A Tutorial
    @ECML-PKDD 2022. August 23, 2022
  6. Deep Learning on Graphs
    @DeepLearn 2022 Summer School. July 25-29, 2022
  7. Deep Learning on Graphs
    @ICONIP 2020, Nov 18, 2020
  8. Hyperbolic Representation Learning in Recommender System and Quant
    @Learning on Graphs Seminar. August 6, 2022
  9. Hyperbolic Graph Neural Network: Methods and Applications
    @Huawei GNN Workshop Day. May 6, 2022
  10. Hyperbolic Graph Neural Network for Recommender System
    @Search and Recommendation Section, Huawei Noah's Ark Lab. March 16, 2022

Professional Services