SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

Understand

Graph neural networks (GNNs) work well when the graph structure is provided.

• However, this structure may not always be available in real-world applications.
• One solution to this problem is to infer a task-specific latent structure and then apply a GNN to the inferred graph.
• Unfortunately, the space of possible graph structures grows super-exponentially with the number of nodes and so the task-specific supervision may be insufficient for learning both the structure and the GNN parameters.