Graph Imbalance

Graph imbalance, a prevalent issue in graph-structured data, arises from uneven distributions of node degrees, class labels, or topological structures, hindering the performance of graph neural networks (GNNs). Current research focuses on addressing this imbalance through various techniques, including cost-sensitive learning, graph augmentation strategies that selectively add or remove edges to balance the graph structure, and the development of specialized GNN architectures that incorporate centrality measures or expert models to handle imbalanced data. Overcoming graph imbalance is crucial for improving the accuracy and robustness of GNNs across diverse applications, such as fraud detection in telecommunications and social networks, and generally enhancing the reliability of graph-based machine learning models.