11 citations · 20 across the 6 of their papers we have counts for
4 papers · 1 filter
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction
Peizhen Bai, Xianyuan Liu, Haiping Lu
Molecular property prediction with deep learning has gained much attention over the past years. Owing to the scarcity of labeled molecules, there has been growing interest in self-…
Hop-Hop Relation-aware Graph Neural Networks
Li Zhang, Yan Ge, Haiping Lu
Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either homogeneous or heterogeneous graphs. In this paper,…
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
Hao Xu, Shengqi Sang, Haiping Lu
The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and protei…
Mixed-Order Spectral Clustering for Networks
Yan Ge, Haiping Lu, Pan Peng
Clustering is fundamental for gaining insights from complex networks, and spectral clustering (SC) is a popular approach. Conventional SC focuses on second-order structures (e.g.,…