Graph AutoEncoder

Graph autoencoders (GAEs) are neural network models that learn compressed representations of graph data by encoding the graph into a lower-dimensional latent space and then decoding it back to reconstruct the original graph structure and/or node features. Current research focuses on improving GAE architectures for specific tasks, such as anomaly detection, link prediction, and node classification, often incorporating techniques like masked autoencoding, contrastive learning, and the use of graph neural networks within the encoder-decoder framework. These advancements enhance the ability of GAEs to handle various graph types (signed, dynamic, textual) and address challenges like scalability and interpretability, leading to improved performance in diverse applications ranging from social network analysis to scientific machine learning.