Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

Understand

Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications.

• In particular, graph neural networks (GNNs), which take numerical node features and graph structure as inputs, have been shown to achieve state-of-the-art performance on various graph-related learning tasks.
• Recent works exploring the correlation between numerical node features and graph structure via self-supervised learning have paved the way for further performance improvements of GNNs.
• However, methods used for extracting numerical node features from raw data are still graph-agnostic within standard GNN pipelines.