  Graph representation learning methods generate numerical vector
representations for the nodes in a network, thereby enabling their use in
standard machine learning models. These methods aim to preserve relational
information, such that nodes that are similar in the graph are found close to
one another in the representation space. Similarity can be based largely on one
of two notions: connectivity or structural role. In tasks where node structural
role is important, connectivity based methods show poor performance. Recent
work has begun to focus on scalability of learning methods to massive graphs of
millions to billions of nodes and edges. Many unsupervised node representation
learning algorithms are incapable of scaling to large graphs, and are unable to
generate node representations for unseen nodes. In this work, we propose
Inferential SIR-GN, a model which is pre-trained on random graphs, then
computes node representations rapidly, including for very large networks. We
demonstrate that the model is able to capture node's structural role
information, and show excellent performance at node and graph classification
tasks, on unseen networks. Additionally, we observe the scalability of
Inferential SIR-GN is comparable to the fastest current approaches for massive
graphs.
