A deep latent space model for interpretable representation learning on directed graphs
A deep latent space model for interpretable representation learning on directed graphs is a scholarly work, published in 2024 in ''Neurocomputing''. The main subjects of the publication include political representation, random graph, complex network, deep learning, graph, artificial intelligence, interpretability, leverage, directed acyclic graph, theoretical computer science, graph neural network, gene regulatory network, statistical relational learning, feature learning, machine learning, computer science, and generative model. To leverage both the good interpretability of random graph models and the powerful representation learning ability of deep learning-based methods such as graph neural networks (GNNs), some research proposes deep generative methods by combining the SBMs and GNNs.