TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks

Understand

Training graph neural networks (GNNs) on large-scale graphs can be challenging due to the high computational expense caused by the massive number of nodes and high-dimensional nodal features.

• Existing graph condensation studies tackle this problem only by reducing the number of nodes in the graph.
• However, the resulting condensed graph data can still be cumbersome.
• Specifically, although the nodes of the Citeseer dataset are reduced to 0.9% (30 nodes) in training, the number of features is 3,703, severely exceeding the training sample magnitude.