Min Xie — accepted AI-conference papers

2026

DeloopSGNN: Revisiting Spectral GNNs Through the Lens of Spatial Aggregation

Duanyu Li, Huijun Wu, Min Xie, Kai Lu, Wenzhe Zhang, Zhenwei Wu +2

AAAI 2026technical

Graph Neural Networks (GNNs) have been studied from two primary perspectives: spectral, which employs global graph signal filtering and is theoretically more expressive, and spatial, which builds on local neighborhood aggregation and generalizes well across diverse graph structures. While spectral G

Cited by 0 Source ↗PDF ↗Scholar ↗

2026

UniPocket: Physics-Aware Geometric Graph Learning with Manifold Completeness for Ligand-Specific Binding Site Prediction

Kangxin Chen, Jieyu Zhao, Jinli Hu, Min Xie

IJCAI 2026

Predicting ligand binding sites on protein surfaces requires capturing complex local geometries and satisfying physical constraints. Existing voxel-based methods suffer from high computational costs and rotation sensitivity, while standard point-cloud GNNs often lack geometric completeness—failing t

Cited by 0 Scholar ↗