Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification

arXiv:2403.17500v1 Announce Type: new
Abstract: Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference. In recent years, graph neural networks (GNNs) have emerged as powerful graph models for inductive learning tasks such as node classification, whereas they typically heavily rely on the annotated nodes under a fully supervised training setting. Compared with the GNN-based methods, …

abstract applications arxiv auto classification cs.lg domains encoder gnns graph graph neural networks graph representation inductive inference issue networks neural networks representation representation learning research semi-supervised type