Geometric deep learning has demonstrated a great potential in non-Euclidean
data analysis. The incorporation of geometric insights into learning
architecture is vital to its success. Here we propose a curvature-enhanced
graph convolutional network (CGCN) for biomolecular interaction prediction, for
the first time. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize
network local structures and to enhance the learning capability of GCNs. More
specifically, ORCs are evaluated based on the local topology from node
neighborhoods, and further used as weights for the feature aggregation in
message-passing procedure. Our CGCN model is extensively validated on fourteen
real-world bimolecular interaction networks and a series of simulated data. It
has been found that our CGCN can achieve the state-of-the-art results. It
outperforms all existing models, as far as we know, in thirteen out of the
fourteen real-world datasets and ranks as the second in the rest one. The
results from the simulated data show that our CGCN model is superior to the
traditional GCN models regardless of the positive-to-negativecurvature ratios,
network densities, and network sizes (when larger than 500).

本文提出了一种曲率增强的图卷积神经网络，使用 Ollivier-Ricci 曲率为节点邻域的特征聚合赋权，用于生物分子相互作用预测，结果显示该模型表现优异。