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arXiv Machine Learning By Resul Tugay, Eren Olu\u{g}, Elif Ak, Sule Gunduz Oguducu

GraphK: Variable-Size Graph Generation with Efficient Edge Construction

Read the original on arXiv Machine Learning →

GraphK introduces an encoder‑sampler‑decoder framework that generates variable‑size graphs efficiently. It learns permutation‑invariant latent representations and samples new node embeddings via maximum likelihood, enabling both upscaling and downscaling of graph size. Edge construction uses KDTree‑based top‑k neighbor search in latent space, reducing computational cost while capturing graph properties.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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