Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
<|reference_start|>Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks: Although many real-world applications, such as disease prediction, and fault detection suffer from class imbalance, most existing graph-based classification methods ignore the skewness of the distribution of classes; therefore, tend to be biased towards the majority class(es). Conventional methods typically tackle this problem through the assignment of weights to each one of the class samples based on a function of their loss, which can lead to over-fitting on outliers. In this paper, we propose a meta-learning algorithm, named Meta-GCN, for adaptively learning the example weights by simultaneously minimizing the unbiased meta-data set loss and optimizing the model weights through the use of a small unbiased meta-data set. Through experiments, we have shown that Meta-GCN outperforms state-of-the-art frameworks and other baselines in terms of accuracy, the area under the receiver operating characteristic (AUC-ROC) curve, and macro F1-Score for classification tasks on two different datasets.<|reference_end|>
@article{mohammadizadeh2024meta-gcn:,
    title={Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing
  with the Data Imbalance in Graph Neural Networks},
    author={Mahdi Mohammadizadeh, Arash Mozhdehi, Yani Ioannou, Xin Wang},
    journal={arXiv preprint arXiv:2406.17073},
    year={2024},
    doi={10.21428/594757DB.0041F830},
    archivePrefix={arXiv},
    eprint={2406.17073},
    primaryClass={cs.LG cs.AI}
}