Topology Imbalance

Topological imbalance in graph-structured data refers to uneven distributions of nodes or edges across different classes, hindering the performance of graph neural networks (GNNs) in tasks like node and edge classification. Current research focuses on developing methods to mitigate this imbalance, including novel metrics to quantify it (e.g., topological entropy), and algorithms that re-weight training data or synthesize new edges to balance the representation of different topological structures. Addressing topological imbalance is crucial for improving the reliability and accuracy of GNNs across various applications, such as social network analysis, cybersecurity, and biomedical knowledge graph analysis, where biased representations can lead to misleading or biologically meaningless predictions.