The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal-Organic Framework Networks
Abstract
The expansion of large-scale materials databases has facilitated the development of graph-based representations, encoding structural and functional similarities as edges in data-driven networks. These enable machine learning models to leverage both local features and global relationships. However, densely connected datasets often introduce redundancy and noise, escalating computational complexity without improving performance. Here, we introduce the Black Hole Strategy, a gravity-based representative sampling method that constructs compact, informative subsets from large materials datasets while preserving essential structural and property diversity. Using metal-organic frameworks (MOFs) as a case study, we demonstrate that graph neural networks (GraphSAGE, GCN, and GAT) trained on Black Hole-sparsified datasets achieve comparable or superior classification and regression performance compared to full-dataset models, despite utilizing significantly fewer data points and reduced memory and training time requirements. Analysis of class-level confusion matrices confirms that critical structure-property relationships─such as pore-limiting diameter (PLD)─persist under substantial sparsification. An ablation study on gravity score weights validates the balanced formulation and robustness of the approach. Topological and efficiency benchmarks further demonstrate that the method preserves modularity, diversity, and connectivity across sparsification levels. These findings establish…
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