  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) with an equivalent weight space, by setting the trainable parameters
with the same shapes, making us curious about \textbf{\emph{how do GNNs using
weights from a fully trained PeerMLP perform?}} Surprisingly, we find that GNNs
initialized with such weights significantly outperform their PeerMLPs,
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 $7.97\%$ improvement for GraphSAGE across $7$ datasets for node
classification, and up to $17.81\%$ improvement across $4$ datasets for link
prediction on metric Hits@10). The code is available at
\href{https://github.com/snap-research/MLPInit-for-GNNs}.
