  Node classification is a central task in graph data analysis. Scarce or even
no labeled data of emerging classes is a big challenge for existing methods. A
natural question arises: can we classify the nodes from those classes that have
never been seen? In this paper, we study this zero-shot node classification
(ZNC) problem which has a two-stage nature: (1) acquiring high-quality class
semantic descriptions (CSDs) for knowledge transfer, and (2) designing a well
generalized graph-based learning model. For the first stage, we give a novel
quantitative CSDs evaluation strategy based on estimating the real class
relationships, so as to get the "best" CSDs in a completely automatic way. For
the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN)
method, following the principles of locality and compositionality for zero-shot
model generalization. Finally, we conduct extensive experiments to demonstrate
the effectiveness of our solutions.
