2021/01/28 by Cheng Mao, Mao, Cheng, Mark Rudelson +3 · 2 citations
Computer Science · Decision Sciences · Physics and Astronomy · #Complex Network Analysis Techniques #Data Quality and Management #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Graph Theory and Algorithms #Machine Learning (stat.ML) #Probability (math.PR) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2101.11783
openalex publication_date 2021/01/28 · openalex created_date 2021/02/01 · openalex updated_date 2026/07/28
Graph matching, also known as network alignment, refers to finding a bijection between the vertex sets of two given graphs so as to maximally align their edges. This fundamental computational problem arises frequently in multiple fields such as computer vision and biology. Recently, there has been a plethora of work studying efficient algorithms for graph matching under probabilistic models. In this work, we propose a new algorithm for graph matching: Our algorithm associates each vertex with a signature vector using a multistage procedure and then matches a pair of vertices from the two graphs if their signature vectors are close to each other. We show that, for two Erdős--Rényi graphs with edge correlation 1-α, our algorithm recovers the underlying matching exactly with high probability when α≤ 1 / (log log n)C, where n is the number of vertices in each graph and C denotes a positive universal constant. This improves the condition α≤ 1 / (log n)C achieved in previous work.