Vertex nomination via seeded graph matching. (16th March 2020)

• Record Type: Journal Article
• Authors: Patsolic, Heather G.; Park, Youngser; Lyzinski, Vince; Priebe, Carey E.
• Is Part Of: Statistical analysis and data mining. Volume 13:Number 3(2020)
• Page Start: 229
• Page End: 244
• Publication Date: 2020-03-16
• Subjects: graph inference -- graph matching -- graph mining -- seeded graph matching -- stochastic block model -- vertex nomination; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11454
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: Consider two networks on overlapping, nonidentical vertex sets. Given vertices of interest (VOIs) in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences, herein we present a principled methodology appropriate for situations in which the networks are too large/noisy for brute‐force graph matching. Our methodology identifies vertices in a local neighborhood of the VOIs in the first network that have verifiable corresponding vertices in the second network. Leveraging these known correspondences, referred to as seeds, we match the induced subgraphs in each network generated by the neighborhoods of these verified seeds, and rank the vertices of the second network in terms of the most likely matches to the original VOIs. We demonstrate the applicability of our methodology through simulations and real data examples.