Understanding Negative Sampling in Graph Representation Learning

Understand

Graph representation learning has been extensively studied in recent years.

• Despite its potential in generating continuous embeddings for various networks, both the effectiveness and efficiency to infer high-quality representations toward large corpus of nodes are still challenging.
• Sampling is a critical point to achieve the performance goals.
• Prior arts usually focus on sampling positive node pairs, while the strategy for negative sampling is left insufficiently explored.