Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction

TL;DR

This paper introduces a novel curvature-enhanced graph convolutional network (CGCN) that utilizes Ollivier-Ricci curvature to improve biomolecular interaction prediction, achieving state-of-the-art results on multiple real-world and simulated datasets.

Contribution

The paper presents the first integration of Ollivier-Ricci curvature into GCNs for biomolecular interaction prediction, enhancing learning capability and performance.

Findings

CGCN outperforms existing models on 13 of 14 real-world datasets.

CGCN achieves superior results across various simulated network conditions.

The model maintains high performance regardless of network density and size.

Abstract

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).