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.

中文翻译：

---

具有完全不平衡标签的网络嵌入

网络嵌入，旨在将网络投影到低维空间，正日益成为网络研究的重点。半监督网络嵌入利用标记数据，并已显示出良好的性能。然而，现有的半监督方法在完全不平衡的标签设置中会得到不吸引人的结果，其中一些类根本没有标签节点。为了缓解这种情况，我们提出了两种新颖的半监督网络嵌入方法。第一个是名为 RSDNE 的浅层方法。具体来说，为了从完全不平衡的标签中受益，RSDNE 以近似的方式保证了类内相似性和类间不相似性。另一种方法是 RECT，它是一类新的图神经网络。与RSDNE不同，从完全不平衡的标签中受益，RECT 探索类语义知识。这使 RECT 能够处理具有节点特征和多标签设置的网络。在几个真实世界数据集上的实验结果证明了所提出方法的优越性。