Scalable Neural Network Training over Distributed Graphs

Graph neural networks (GNNs) fuel diverse machine learning tasks involving graph-structured data, ranging from predicting protein structures to serving personalized recommendations. Real-world graph data must often be stored distributed across many machines not just because of capacity constraints, but because of compliance with data residency or privacy laws. In such setups, network communication is costly and becomes the main bottleneck to train GNNs. Optimizations for distributed GNN training have targeted data-level improvements so far -- via caching, network-aware partitioning, and …

capacity communication compliance constraints cs.ai cs.ir cs.lg data data residency distributed diverse gnns graph graph data graph neural networks graphs laws machine machine learning machines network networks network training neural network neural networks personalized personalized recommendations privacy privacy laws protein protein structures recommendations residency scalable structured data tasks training world