  Zero-shot graph embedding is a major challenge for supervised graph learning.
Although a recent method RECT has shown promising performance, its working
mechanisms are not clear and still needs lots of training data. In this paper,
we give deep insights into RECT, and address its fundamental limits. We show
that its core part is a GNN prototypical model in which a class prototype is
described by its mean feature vector. As such, RECT maps nodes from the
raw-input feature space into an intermediate-level semantic space that connects
the raw-input features to both seen and unseen classes. This mechanism makes
RECT work well on both seen and unseen classes, which however also reduces the
discrimination. To realize its full potentials, we propose two label expansion
strategies. Specifically, besides expanding the labeled node set of seen
classes, we can also expand that of unseen classes. Experiments on real-world
datasets validate the superiority of our methods.
