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Graph Autoencoder
Unsupervised neural network architecture composed of an encoder and a decoder that learns to compress and reconstruct the structure and attributes of a graph.
Graph Reconstruction
Process aimed at reconstructing the adjacency matrix or attributes of the original graph from the compressed latent representation.
Latent Space
Low-dimensional representation space where the encoder projects graph information, capturing essential features for reconstruction.
Encoder Network
Part of the autoencoder that transforms graph data into a compact representation in the latent space through message passing operations.
Decoder Network
Component that reconstructs the original graph from the latent representation by predicting missing edges and node attributes.
Adjacency Matrix Reconstruction
Specific reconstruction task aimed at predicting the adjacency matrix of the original graph from latent embeddings.
Feature Reconstruction
Reconstruction objective for node or edge features in addition to the topological structure of the graph.
Graph Convolutional Autoencoder
Autoencoder variant using graph convolutional layers to capture local and global dependencies in the graph structure.
Variational Graph Autoencoder
Probabilistic extension of graph autoencoders using a variational approach to learn a distribution over the latent space.
Deep Graph Infomax
Unsupervised learning method that maximizes mutual information between global and local graph representations.
Graph Representation Learning
Machine learning paradigm aimed at discovering meaningful vector representations of graph entities without labels.
Node Clustering
Application of graph autoencoders to group nodes into communities based on their learned latent embeddings.
Graph Regularization
Technique that constrains the latent space to preserve the original graph structure by penalizing representations that distort topological relationships.
Graph Contrastive Learning
Approach for learning graph representations by maximizing consistency between different augmentations of the same graph.
Graph Denoising Autoencoder
Robust variant that learns to reconstruct a clean graph from a version corrupted by structural or attribute noise.
Graph Attention Autoencoder
Architecture incorporating attention mechanisms to weight neighbor contributions differently during encoding and decoding.