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Creators/Authors contains: "Moniz, Nuno"

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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. ; ; ; ; ; ; (, ACM)
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