  Network node embedding is an active research subfield of complex network
analysis. This paper contributes a novel approach to learning network node
embeddings and direct node classification using a node ranking scheme coupled
with an autoencoder-based neural network architecture. The main advantages of
the proposed Deep Node Ranking (DNR) algorithm are competitive or better
classification performance, significantly higher learning speed and lower space
requirements when compared to state-of-the-art approaches on 15 real-life node
classification benchmarks. Furthermore, it enables exploration of the
relationship between symbolic and the derived sub-symbolic node
representations, offering insights into the learned node space structure. To
avoid the space complexity bottleneck in a direct node classification setting,
DNR computes stationary distributions of personalized random walks from given
nodes in mini-batches, scaling seamlessly to larger networks. The scaling laws
associated with DNR were also investigated on 1488 synthetic Erd\H{o}s-R\'enyi
networks, demonstrating its scalability to tens of millions of links.
