  Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing
with non-Euclidean structural data. Both spatial-based and spectral-based GNNs
are relying on adjacency matrix to guide message passing among neighbors during
feature aggregation. Recent works have mainly focused on powerful message
passing modules, however, in this paper, we show that none of the message
passing modules is necessary. Instead, we propose a pure
multilayer-perceptron-based framework, Graph-MLP with the supervision signal
leveraging graph structure, which is sufficient for learning discriminative
node representation. In model-level, Graph-MLP only includes multi-layer
perceptrons, activation function, and layer normalization. In the loss level,
we design a neighboring contrastive (NContrast) loss to bridge the gap between
GNNs and MLPs by utilizing the adjacency information implicitly. This design
allows our model to be lighter and more robust when facing large-scale graph
data and corrupted adjacency information. Extensive experiments prove that even
without adjacency information in testing phase, our framework can still reach
comparable and even superior performance against the state-of-the-art models in
the graph node classification task.
