Graph Representation Learning Beyond Node and Homophily

Li, You; Lin, Bei; Luo, Binli; Gui, Ning

doi:10.1109/tkde.2022.3146270

Cited by 6 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many real-world graphs, such as transaction networks [45], exhibit heterophily. Recent studies have shown that GNNs do not perform well on heterophilic graphs [46][47][48][49]. This is because GNNs are typically designed to learn from homophilic graphs, where linked nodes have similar features and class labels.…”

Section: Heterophily-based Gnnsmentioning

confidence: 99%

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

Wu,

Chen,

Cheng

et al. 2023

BMC Bioinformatics

View full text Add to dashboard Cite

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.

show abstract