  Based on the message-passing paradigm, there has been an amount of research
proposing diverse and impressive feature propagation mechanisms to improve the
performance of GNNs. However, less focus has been put on feature
transformation, another major operation of the message-passing framework. In
this paper, we first empirically investigate the performance of the feature
transformation operation in several typical GNNs. Unexpectedly, we notice that
GNNs do not completely free up the power of the inherent feature transformation
operation. By this observation, we propose the Bi-directional Knowledge
Transfer (BiKT), a plug-and-play approach to unleash the potential of the
feature transformation operations without modifying the original architecture.
Taking the feature transformation operation as a derived representation
learning model that shares parameters with the original GNN, the direct
prediction by this model provides a topological-agnostic knowledge feedback
that can further instruct the learning of GNN and the feature transformations
therein. On this basis, BiKT not only allows us to acquire knowledge from both
the GNN and its derived model but promotes each other by injecting the
knowledge into the other. In addition, a theoretical analysis is further
provided to demonstrate that BiKT improves the generalization bound of the GNNs
from the perspective of domain adaption. An extensive group of experiments on
up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% -
4% performance gain over the original GNN, which means a boosted GNN is
obtained. Meanwhile, the derived model also shows a powerful performance to
compete with or even surpass the original GNN, enabling us to flexibly apply it
independently to some other specific downstream tasks.
