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t-SNE (t-Distributed Stochastic Neighbor Embedding) is a non-linear dimensionality reduction technique often used to visualize high-dimensional graph embeddings in lower-dimensional spaces (e.g., 2D or 3D).

Graph Embedding is the process of transforming nodes, edges, and their features into a vector space while preserving the graph's topological structure.

Graph Autoencoders are unsupervised learning models that aim to encode graph data into a latent space and then reconstruct the graph structure from this latent representation.

GCNs are neural networks that operate directly on graphs and generalize convolutional neural networks to graph-structured data.

Node2Vec is an algorithm to learn continuous feature representations for nodes in a graph based on flexible notions of a node's network neighborhood.