Semi-Supervised Graph Learning Meets Dimensionality Reduction

• URL: http://arxiv.org/abs/2203.12522v1
• Date: Wed, 23 Mar 2022 16:31:53 GMT
• Title: Semi-Supervised Graph Learning Meets Dimensionality Reduction
• Authors: Alex Morehead, Watchanan Chantapakul, Jianlin Cheng
• Abstract summary: Semi-supervised learning (SSL) has recently received increased attention from machine learning researchers. 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) Our benchmarks and clustering visualizations demonstrate that, under certain conditions, employing a priori and a posteriori dimensionality reduction to GNN inputs and outputs, respectively, can simultaneously improve the effectiveness of semi-supervised node label propagation and node clustering.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised learning (SSL) has recently received increased attention from machine learning researchers. By enabling effective propagation of known labels in graph-based deep learning (GDL) algorithms, SSL is poised to become an increasingly used technique in GDL in the coming years. However, there are currently few explorations in the graph-based SSL literature on exploiting classical dimensionality reduction techniques for improved label propagation. 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. Our study makes use of benchmark semi-supervised GDL datasets such as the Cora and Citeseer datasets to allow meaningful comparisons of the representations learned by each algorithm when paired with a dimensionality reduction technique. Our comprehensive benchmarks and clustering visualizations quantitatively and qualitatively demonstrate that, under certain conditions, employing a priori and a posteriori dimensionality reduction to GNN inputs and outputs, respectively, can simultaneously improve the effectiveness of semi-supervised node label propagation and node clustering. Our source code is freely available on GitHub.

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