  Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm
become the dominant way to learn on graphic data. Models in this paradigm have
to spend extra space to look up adjacent nodes with adjacency matrices and
extra time to aggregate multiple messages from adjacent nodes. To address this
issue, we develop a method called LinkDist that distils self-knowledge from
connected node pairs into a Multi-Layer Perceptron (MLP) without the need to
aggregate messages. Experiment with 8 real-world datasets shows the MLP derived
from LinkDist can predict the label of a node without knowing its adjacencies
but achieve comparable accuracy against GNNs in the contexts of semi- and
full-supervised node classification. Moreover, LinkDist benefits from its
Non-Message Passing paradigm that we can also distil self-knowledge from
arbitrarily sampled node pairs in a contrastive way to further boost the
performance of LinkDist.
