Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning

Understand

Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains.

• The crux of these embeddings is that they are pretrained on huge corpus of data in a unsupervised fashion, sometimes aided with transfer learning.
• However currently in the graph learning domain, embeddings learned through existing graph neural networks (GNNs) are task dependent and thus cannot be shared across different datasets.
• In this paper, we present a first powerful and theoretically guaranteed graph neural network that is designed to learn task-independent graph embeddings, thereafter referred to as deep universal graph embedding (DUGNN).