  Network alignment, the process of finding correspondences between nodes in
different graphs, has many scientific and industrial applications. Existing
unsupervised network alignment methods find suboptimal alignments that break up
node neighborhoods, i.e. do not preserve matched neighborhood consistency. To
improve this, we propose CONE-Align, which models intra-network proximity with
node embeddings and uses them to match nodes across networks after aligning the
embedding subspaces. Experiments on diverse, challenging datasets show that
CONE-Align is robust and obtains 19.25% greater accuracy on average than the
best-performing state-of-the-art graph alignment algorithm in highly noisy
settings.
