Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks

• URL: http://arxiv.org/abs/2406.17073v2
• Date: Thu, 27 Jun 2024 18:15:16 GMT
• Title: Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks
• Authors: Mahdi Mohammadizadeh, Arash Mozhdehi, Yani Ioannou, Xin Wang,
• Abstract summary: We propose a meta-learning algorithm, named Meta-GCN, for adaptively learning the example weights. We have shown that Meta-GCN outperforms state-of-the-art frameworks and other baselines in terms of accuracy.
• Score: 5.285761906707429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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