Consistent Network Alignment with Node Embedding
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by
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, Danai Koutra
2020
Abstract
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.
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