Graph Embeddings

VERtex Similarity Embeddings

Introduced by Tsitsulin et al. in VERSE: Versatile Graph Embeddings from Similarity Measures

VERtex Similarity Embeddings (VERSE) is a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network.

Source: Tsitsulin et al.

Image source: Tsitsulin et al.

Source: VERSE: Versatile Graph Embeddings from Similarity Measures

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