# Graph matching beyond perfectly-overlapping ErdHos--R'enyi random graphs

arXiv: https://arxiv.org/abs/2006.03284 · HTML: https://mlsanity.com/paper/2006.03284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [A Theory of Unsupervised Translation Motivated by Understanding Animal Communication](https://mlsanity.com/paper/2211.11081/) (2022)
- [Wasserstein gradient flow for optimal probability measure decomposition](https://mlsanity.com/paper/2406.00914/) (2024)

## Abstract (arXiv)

Graph matching is a fruitful area in terms of both algorithms and theories. In this paper, we exploit the degree information, which was previously used only in noiseless graphs and perfectly-overlapping ErdHos--R'enyi random graphs matching. We are concerned with graph matching of partially-overlapping graphs and stochastic block models, which are more useful in tackling real-life problems. We propose the edge exploited degree profile graph matching method and two refined varations. We conduct a thorough analysis of our proposed methods' performances in a range of challenging scenarios, including a zebrafish neuron activity data set and a coauthorship data set. Our methods are proved to be numerically superior than the state-of-the-art methods.