Network alignment, the process of finding correspondences between nodes in different graphs, has significant scientific and industrial applications. We find that many existing network alignment methods fail to achieve accurate alignments because they break up node neighborhoods during alignment, failing to preserve matched neighborhood consistency. To improve this, we propose CONE-Align, which matches nodes based on embeddings that model intra-network proximity and are aligned to be comparable across networks . Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .
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 .
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