Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks

Researchers investigated deep learning methods for graph neural networks to predict HIV infections with social network information. Using network data from two cohorts of men in different cities, researchers used GNNExplainer to determine feature importance from graph attention network (GAT) models. Their findings reinforced potential application of GAT models in predicting HIV infections.