Inferential SIR-GN: Scalable Graph Representation Learning

Understand

Graph representation learning methods generate numerical vector representations for the nodes in a network, thereby enabling their use in standard machine learning models.

• These methods aim to preserve relational information, such that nodes that are similar in the graph are found close to one another in the representation space.
• Similarity can be based largely on one of two notions: connectivity or structural role.
• In tasks where node structural role is important, connectivity based methods show poor performance.