Motif Based Graph

Motif-based graph analysis focuses on identifying and utilizing recurring subgraph patterns (motifs) within larger graphs to improve model performance and interpretability. Current research emphasizes developing graph neural network (GNN) architectures that effectively incorporate motif information for tasks like node classification, link prediction, and anomaly detection, often employing techniques like motif-preserving graph generation and curriculum learning to address data imbalances and improve efficiency. This approach enhances the explainability of GNNs, particularly in domains like molecular science and finance, while also improving predictive accuracy by leveraging higher-order structural information often missed by traditional methods.