graph-embeddings

SAGE-XGBoost is a machine learning framework that uses spatially augmented graph embeddings and data augmentation to enhance natural hazard susceptibility mapping under data scarcity, demonstrating superior performance over conventional models.

This paper analyzes distance-preserving embeddings in inhomogeneous random graphs, providing tighter distortion bounds than classical worst-case results and introducing a GNN-augmented variant that learns universal features from small graphs.

MABLE combines masked reconstruction with cosine-similarity losses to learn node and graph embeddings from large heterogeneous graphs, demonstrated on geospatial mineral-exploration data. It unifies masked autoencoding and metric learning in a self-supervised framework without requiring labeled data.