Online Program Home
My Program

Abstract Details

Activity Number: 413 - Network Analysis and Network-Based Modeling
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #307053
Title: Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Author(s): Jesus Arroyo* and Daniel L Sussman and Carey E Priebe and Vince Lyzinski
Companies: Johns Hopkins University and Boston University and Johns Hopkins University and University of Massachusetts Amherst
Keywords: graph matchability; corrupting channel; consistency

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study this problem from a statistical framework in which one of the graphs is an errorfully observed copy of the other. We introduce a corrupting channel model, and show that in this model framework, the solution to the graph matching problem is a maximum likelihood estimator. Necessary and sufficient conditions for consistency of this MLE are presented, as well as a relaxed notion of consistency in which a negligible fraction of the vertices need not be matched correctly. The results are used to study matchability in several families of random graphs, including edge independent models, random regular graphs and small-world networks. We also use these results to introduce measures of matching feasibility, and experimentally validate the results on simulated and real-world networks.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program