Semi-Supervised Graph Learning Meets Dimensionality Reduction

1 code implementation • 23 Mar 2022 • Alex Morehead, Watchanan Chantapakul, Jianlin Cheng

In this work, we investigate the use of dimensionality reduction techniques such as PCA, t-SNE, and UMAP to see their effect on the performance of graph neural networks (GNNs) designed for semi-supervised propagation of node labels.

Clustering Dimensionality Reduction +2