Dynamic Graph Sampling Service for Realtime GNN Inference at Scale
Graph neural networks(GNNs) learn graph vertex representations by aggregating multi-hop neighbor information. Industrial applications often adopt mini-batch training to scale out GNNs on large-scale graphs, where neighbor sampling is used during both model training and inference. Since the structure and attributes of real-world graphs often change dynamically, it is imperative that the inferred vertex representation can accurately reflect these updates.