Consistent polynomial-time unseeded graph matching for Lipschitz graphons
This solves a long-standing statistical challenge in graph matching, enabling efficient network analysis for applications like social networks or biology.
The authors tackled the unseeded node matching problem for networks with smooth structures by proposing a consistent polynomial-time method, achieving a breakthrough where previous solutions required quasi-polynomial time.
We propose a consistent polynomial-time method for the unseeded node matching problem for networks with smooth underlying structures. Despite widely conjectured by the research community that the structured graph matching problem to be significantly easier than its worst case counterpart, well-known to be NP-hard, the statistical version of the problem has stood a challenge that resisted any solution both provable and polynomial-time. The closest existing work requires quasi-polynomial time. Our method is based on the latest advances in graphon estimation techniques and analysis on the concentration of empirical Wasserstein distances. Its core is a simple yet unconventional sampling-and-matching scheme that reduces the problem from unseeded to seeded. Our method allows flexible efficiencies, is convenient to analyze and potentially can be extended to more general settings. Our work enables a rich variety of subsequent estimations and inferences.