• Corpus ID: 246285324

On the Power of Gradual Network Alignment Using Dual-Perception Similarities

@article{Park2022OnTP,
  title={On the Power of Gradual Network Alignment Using Dual-Perception Similarities},
  author={Jin-Duk Park and Cong Tran and Won-Yong Shin and Xin Cao},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.10945}
}
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA… 

Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation

TLDR
Experimental results demonstrate that Grad-Align+ exhibits a) superiority over benchmark NA methods, (b) empirical validation of the theoretical findings, and (c) the effectiveness of the attribute augmentation module.

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