Few-Shot Learning on Graphs

Understand

Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications.

• However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming.
• In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
• There have been many studies working on FSLG recently.