Before revision:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable  graph pooling method that takes into account the node features and graph structure to improve molecular understanding. Inspired by theories in graph signal processing, LaPool performs a feature-driven hierarchical segmentation of molecules by selecting a set of centroid nodes from a graph as cluster representatives. It then learns a sparse assignment of remaining nodes into these clusters using an attention mechanism. We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and prediction tasks.
After revision:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable graph pooling method that takes into account the node features and graph structure to improve molecular representation. We benchmark LaPool on molecular graph understanding and prediction tasks.