  Network embedding, aiming to project a network into a low-dimensional space,
is increasingly becoming a focus of network research. Semi-supervised network
embedding takes advantage of labeled data, and has shown promising performance.
However, existing semi-supervised methods would get unappealing results in the
completely-imbalanced label setting where some classes have no labeled nodes at
all. To alleviate this, we propose two novel semi-supervised network embedding
methods. The first one is a shallow method named RSDNE. Specifically, to
benefit from the completely-imbalanced labels, RSDNE guarantees both
intra-class similarity and inter-class dissimilarity in an approximate way. The
other method is RECT which is a new class of graph neural networks. Different
from RSDNE, to benefit from the completely-imbalanced labels, RECT explores the
class-semantic knowledge. This enables RECT to handle networks with node
features and multi-label setting. Experimental results on several real-world
datasets demonstrate the superiority of the proposed methods.
