Despite several signs of progress have been made recently, limited research
has been conducted for an inductive setting where embeddings are required for
newly unseen nodes -- a setting encountered commonly in practical applications
of deep learning for graph networks. This significantly affects the
performances of downstream tasks such as node classification, link prediction
or community extraction. To this end, we propose SANNE -- a novel unsupervised
embedding model -- whose central idea is to employ a transformer self-attention
network to iteratively aggregate vector representations of nodes in random
walks. Our SANNE aims to produce plausible embeddings not only for present
nodes, but also for newly unseen nodes. Experimental results show that the
proposed SANNE obtains state-of-the-art results for the node classification
task on well-known benchmark datasets.

我们提出了一种名为 SANNE 的新型无监督嵌入模型，其集中思想是使用转换器自我注意网络来迭代地聚合节点在随机游走中的向量表示，以生成合理的嵌入，不仅适用于现有节点，而且适用于新出现的未见节点。实验结果表明，所提出的 SANNE 在著名的基准数据集上对于节点分类任务获得了最先进的结果。