Generalized heterophily graph data augmentation for node classification

Graph data augmentations have demonstrated remarkable performance on homophilic graph neural networks (GNNs). Nevertheless, when transferred to a heterophilic graph, these augmentations are less effective for GNN models and lead to reduced performance. To address this issue, we propose a unified augmentation approach called GePHo, a regularization technique for heterophilic graph neural networks based on self-supervised learning, leveraging graph data augmentation to acquire extra information to guide model learning. Specifically, we propose to generate a pseudo-homophily graph that is type-agnostic, enabling us to apply GePHo to both homophilic and heterophilic graphs. Then, we regularize the neighbors with a sharpening technique for data augmentation and generate the auxiliary pseudo-labels to classify the original GNN's output, whose operations are to constrain the local and global node representation, respectively. Extensive experiments on three homophilic graph and six heterophilic graph datasets demonstrate the competitive effectiveness of GePHo in node classification task, and the ablation experiments verify the efficacy of our GePHo in graph data augmentation.

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the "off-the-shelf" GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.