VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To scale such GNNs to large graphs, various neighbor-, layer-, or subgraph-sampling techniques are proposed to alleviate the "neighbor explosion" problem by considering only a small subset of messages passed to the nodes in a mini-batch... However, sampling-based methods are difficult to apply to GNNs that utilize many-hops-away or global context each layer, show unstable performance for different tasks and datasets, and do not speed up model inference. We propose a principled and fundamentally different approach, VQ-GNN, a universal framework to scale up any convolution-based GNNs using Vector Quantization (VQ) without compromising the performance. In contrast to sampling-based techniques, our approach can effectively preserve all the messages passed to a mini-batch of nodes by learning and updating a small number of quantized reference vectors of global node representations, using VQ within each GNN layer. Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix. We show that such a compact low-rank version of the gigantic convolution matrix is sufficient both theoretically and experimentally. In company with VQ, we design a novel approximated message passing algorithm and a nontrivial back-propagation rule for our framework. Experiments on various types of GNN backbones demonstrate the scalability and competitive performance of our framework on large-graph node classification and link prediction benchmarks. read more

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Link Property Prediction ogbl-collab VQ-GNN (GCN) Test Hits@50 0.4316 ± 0.0134 # 29
Link Property Prediction ogbl-collab VQ-GNN (SAGE-Mean) Test Hits@50 0.4673 ± 0.0164 . # 26
Link Property Prediction ogbl-collab VQ-GNN (GAT) Test Hits@50 0.4102 ± 0.0099 # 30
Node Classification PPI VQ-GNN (GAT) F1 97.37 # 12
Node Classification Reddit VQ-GNN (SAGE-Mean) Accuracy 94.5 ± .0024 # 7