Graph unpooling is a graph neural network operation that reconstructs higher-resolution graphs from pooled representations — the inverse of pooling, used in graph autoencoders and generative models to upsample graph structures.

What Is Graph Unpooling?

• Definition: Reconstruct graph structure from compressed representation.
• Purpose: Enable graph generation and reconstruction tasks.
• Inverse Of: Graph pooling (which compresses graphs).
• Use Case: Graph autoencoders, generative models, super-resolution.
• Challenge: Recover both node features and edge connectivity.

Why Graph Unpooling Matters

• Graph Generation: Create new molecules, social networks, circuits.
• Reconstruction: Graph autoencoders need unpooling for decoder.
• Super-Resolution: Upsample coarse graphs to finer detail.
• Hierarchical Models: Build multi-scale graph representations.

Unpooling Strategies

• Index-Based: Store pooling indices, use to place nodes.
• Learned Upsampling: Neural network predicts new nodes/edges.
• Spectral Methods: Reconstruct via graph Fourier transform.
• Generative: Sample new structure from learned distribution.

Applications

Molecule generation, circuit design, network synthesis, 3D mesh reconstruction.

Graph unpooling is essential for graph generative models — enabling reconstruction from compressed representations.