Before revision:  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 .
After revision:  To improve this, we propose CONE-Align, which matches nodes based on embeddings that model 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 up to 49 \% greater accuracy than the state-of-the-art graph alignment algorithms .