MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
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Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for graph data, however they usually sacrifice prediction accuracy, limiting their applications for graph data. We observe that for most message passing-based GNNs, we can trivially derive an analog MLP (we call this a PeerMLP) whose weights can be made identical, making us curious about how do GNNs using weights from a fully trained PeerMLP perform? Surprisingly, we find that GNNs initialized with such weights significantly outperform their PeerMLPs for graph data, motivating us to use PeerMLP training as a precursor, initialization step to GNN training. To this end, we propose an embarrassingly simple, yet hugely effective initialization method for GNN training acceleration, called MLPInit. Our extensive experiments on multiple large-scale graph datasets with diverse GNN architectures validate that MLPInit can accelerate the training of GNNs (up to 33X speedup on OGB-products) and often improve prediction performance (e.g., up to 7.97 across 7 datasets for node classification, and up to 17.81 4 datasets for link prediction on metric Hits@10). Most importantly, MLPInit is extremely simple to implement and can be flexibly used as a plug-and-play initialization method for message passing-based GNNs.
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