  Despite Graph Neural Networks (GNNs) have achieved prominent success in many
graph-based learning problem, such as credit risk assessment in financial
networks and fake news detection in social networks. However, the trained GNNs
still make errors and these errors may cause serious negative impact on
society. \textit{Model editing}, which corrects the model behavior on wrongly
predicted target samples while leaving model predictions unchanged on unrelated
samples, has garnered significant interest in the fields of computer vision and
natural language processing. However, model editing for graph neural networks
(GNNs) is rarely explored, despite GNNs' widespread applicability. To fill the
gap, we first observe that existing model editing methods significantly
deteriorate prediction accuracy (up to $50\%$ accuracy drop) in GNNs while a
slight accuracy drop in multi-layer perception (MLP). The rationale behind this
observation is that the node aggregation in GNNs will spread the editing effect
throughout the whole graph. This propagation pushes the node representation far
from its original one. Motivated by this observation, we propose
\underline{E}ditable \underline{G}raph \underline{N}eural \underline{N}etworks
(EGNN), a neighbor propagation-free approach to correct the model prediction on
misclassified nodes. Specifically, EGNN simply stitches an MLP to the
underlying GNNs, where the weights of GNNs are frozen during model editing. In
this way, EGNN disables the propagation during editing while still utilizing
the neighbor propagation scheme for node prediction to obtain satisfactory
results. Experiments demonstrate that EGNN outperforms existing baselines in
terms of effectiveness (correcting wrong predictions with lower accuracy drop),
generalizability (correcting wrong predictions for other similar nodes), and
efficiency (low training time and memory) on various graph datasets.
