Uncovering Structural Hierarchies in Molecules with Rich Club-Informed Representation Learning
Yasida Insika Wanigatunga, Asela Hevapathige · 2025
In this study, we leverage the rich club coefficient to improve the representation capabilities of existing Graph Neural Networks(GNNs) for molecular graph classification. To achieve this, we introduce a novel structural plugin that enhances node features, enabling them to capture the hierarchical structural information within the graph. Our experiments demonstrate that our solution can uplift the performance of existing GNNs.