  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.
