11 LEVERAGING PHENOTYPE DATA TO IDENTIFY POTENTIAL CARRIERS OF DELETERIOUS GENETIC VARIANTS

Abstract

This paper investigates network representation learning which involves network structures and labels. Most methods proposed so far try to utilize different kinds of network related data available in just one perfect model to learn the set of perfect embeddings, and then evaluate its performance comparing with other methods for downstream applications, such as node classification or link prediction. In this paper, we introduce ensemble learning to the study of network representation learning. In conventional scenario, ensemble methods train multiple individual learners and combine all their results about the same input to produce an improved one. Inspired by this, we try to train multiple individual models using network related data, and aggregate all the learned embeddings as the final network representations, to expect a performance boost for downstream applications. In order to learn good and diverse individual models, bootstrap sampling is used and different individual model structures are designed. Experimental results show that the idea of aggregating network embeddings really works, and can outperform existing excellent methods under specific experimental setups.