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Creators/Authors contains: "Tian, Yijun"

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  1. ; ; ; (, ACM Computing Surveys)
    Rapid advancement in machine learning is increasing the demand for effective graph data analysis. However, real-world graph data often exhibits class imbalance, leading to poor performance of standard machine learning models on underrepresented classes. To address this,Class-ImbalancedLearning onGraphs (CILG) has emerged as a promising solution that combines graph representation learning and class-imbalanced learning. This survey provides a comprehensive understanding of CILG’s current state-of-the-art, establishing the first systematic taxonomy of existing work and its connections to traditional imbalanced learning. We critically analyze recent advances and discuss key open problems. A continuously updated reading list of relevant articles and code implementations is available athttps://github.com/yihongma/CILG-Papers. 
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    Free, publicly-accessible full text available August 31, 2026
  2. ; ; ; ; (, The Twelfth International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available December 10, 2025
  3. ; ; ; ; (, The Twelfth International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available November 23, 2025
  4. ; ; ; ; ; (, ACM)
    Full Text Available 
  5. ; ; ; ; (, The Twelfth International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, The Twelfth International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  7. ; ; ; ; (, The Twelfth International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, Association for Computational Linguistics)
    Full Text Available 
  9. ; ; ; ; ; (, Association for Computational Linguistics)
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  10. ; ; ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available