Graphsare an effective data organization method capable of representing complex relationships and structures in the real world. Graph Neural Networks (GNN) extract feature representations of graphs by ...

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised ...

In response to the challenges posed by existing graph neural network methods in capturing global dependencies and diverse representations, as well as the difficulty in fully revealing the inherent ...

The GCATCMDA model proposed in this study is a microbe-disease association prediction model based on graph neural networks and contrastive learning. It aims to predict potential associations between ...

CL-GNN utilizes a contrastive learning strategy, a form of SSL, to learn from a large data set of 371 458 unique unlabeled protein–ligand complexes. By employing graph neural networks and molecular ...

This is the official implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks". In this work, we introduce a contrastive learning framework for molecular ...

Original implementation for paper GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. GCC is a contrastive learning framework that implements unsupervised structural graph ...