Enstitümüz öğretim üyeleri ve öğrencilerinin 'Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders
' başlıklı bildirisi '28th
IEEE conference on signal processing and communications applications' konferansında sunulmuştur. Özet:
Graphs are usually represented by high dimensional data. Hence, graph embedding is an essential task, which aims to represent a graph in a lower dimension while protecting the original graph’s properties. In this paper, we propose a novel graph embedding method called Residual Variational Graph Autoencoder (RVGAE), which boosts variational graph autoencoder’s performance utilizing residual connections. Our method’s performance is evaluated on the link prediction task. The results demonstrate that our model can achieve better results than graph convolutional neural network (GCN) and variational graph autoencoder (VGAE).