Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification) and energy function minimizers that inherit transparent, exploitable inductive biases and interpretability. However, scaling GNN architectures constructed in this way remains challenging, in part because the convergence of the forward pass may involve models with considerable depth. To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence guarantees in certain settings. We also instantiate a full GNN architecture based on these designs, and the model achieves competitive accuracy and scalability when applied to the largest publicly-available node classification benchmark exceeding 1TB in size. Our source code is available at https://github.com/haitian-jiang/MuseGNN.

Graph neural networks (GNNs) are models specialized for graph data and widely used in applications. To train GNNs on large graphs that exceed CPU memory, several systems have been designed to store data on disk and conduct out-of-core processing. However, these systems suffer from either read amplification when conducting random reads for node features that are smaller than a disk page, or degraded model accuracy by treating the graph as disconnected partitions. To close this gap, we build DiskGNN for high I/O efficiency and fast training without model accuracy degradation. The key technique is offline sampling , which decouples graph sampling from model computation . In particular, by conducting graph sampling beforehand for multiple mini-batches, DiskGNN acquires the node features that will be accessed during model computation and conducts pre-processing to pack the node features of each mini-batch contiguously on disk to avoid read amplification for computation. Given the feature access information acquired by offline sampling, DiskGNN also adopts designs including four-level feature store to fully utilize the memory hierarchy of GPU and CPU to cache hot node features and reduce disk access, batched packing to accelerate feature packing during pre-processing, and pipelined training to overlap disk access with other operations. We compare DiskGNN with state-of-the-art out-of-core GNN training systems. The results show that DiskGNN has more than 8x speedup over existing systems while matching their best model accuracy. DiskGNN is open-source at https://github.com/Liu-rj/DiskGNN.