Dataset Recommendation via Variational Graph Autoencoder

Abstract

We frame dataset recommendation as a graph link prediction problem. A variational graph autoencoder learns representations of papers, topics, and datasets from citation structure and document content, then ranks dataset recommendations for a given research query or paper.

Most-cited of my papers (44 citations) — a strong result on dataset-driven scholarly AI.