What advancements have been made in scalable GNN training methods?
Instruction: Discuss recent developments in making GNN training more efficient at scale.
Updated
Example Answer
I would describe scalable GNN methods by what they sample or distribute, rather than claim that one method makes full-graph dependencies disappear. Neighbor sampling limits per-node neighborhoods. FastGCN uses layer-wise importance sampling based on a Monte Carlo view of graph convolution; it does not train each layer independently.
Cluster-GCN trains on graph clusters or combinations of clusters, keeping many local edges within a minibatch. GraphSAINT samples subgraphs and uses normalization to address the sampling procedure. These are established methods from 2018–2020, not evidence that every current workload should use the same approach.
The practical choice depends on graph density, depth, feature size, and storage. I would compare time to a target validation score, peak memory, preprocessing time, and inference cost. Sampling adds variance, and partitions can omit cross-boundary information. Distributed storage can handle a larger graph, but remote feature access and synchronization may become bottlenecks. Any claim about billions of edges or a speedup needs a named benchmark and measured hardware results.
Follow-up
A method with faster minibatches can still take longer to reach the desired quality. Compare complete training runs, not just a single step.