Recent Advances in Efficient and Scalable Graph Neural Networks

A new research blogpost by Chaitanya K. Joshi overviewing the toolbox for Graph Neural Networks to scale to real-world graphs and real-time applications.

Training and deploying GNNs to handle real-world graph data poses several theoretical and engineering challenges:
1. Giant Graphs – Memory Limitations
2. Sparse Computations – Hardware Limitations
3. Graph Subsampling – Reliability Limitations

The blogpost introduces three simple but effective ideas in the 'toolbox' for developing efficient and scalable GNNs:
- Data Preparation - From sampling large-scale graphs to CPU-GPU hybrid training via historical node embedding lookups.
- Efficient Architectures - Graph-augmented MLPs for scaling to giant networks, and efficient graph convolution designs for real-time inference on batches of graph data.
- Learning Paradigms - Combining Quantization Aware Training (low precision model weights and activations) with Knowledge Distillation (improving efficient GNNs using expressive teacher models) for maximizing inference latency as well as performance.

Blogpost: https://www.chaitjo.com/post/efficient-gnns/