16 research outputs found
Seeded Graph Matching: Efficient Algorithms and Theoretical Guarantees
In this paper, a new information theoretic framework for graph matching is
introduced. Using this framework, the graph isomorphism and seeded graph
matching problems are studied. The maximum degree algorithm for graph
isomorphism is analyzed and sufficient conditions for successful matching are
rederived using type analysis. Furthermore, a new seeded matching algorithm
with polynomial time complexity is introduced. The algorithm uses `typicality
matching' and techniques from point-to-point communications for reliable
matching. Assuming an Erdos-Renyi model on the correlated graph pair, it is
shown that successful matching is guaranteed when the number of seeds grows
logarithmically with the number of vertices in the graphs. The logarithmic
coefficient is shown to be inversely proportional to the mutual information
between the edge variables in the two graphs
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
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
Alignment strength and correlation for graphs
When two graphs have a correlated Bernoulli distribution, we prove that the alignment strength of their natural bijection strongly converges to a novel measure of graph correlation ϱT that neatly combines intergraph with intragraph distribution parameters. Within broad families of the random graph parameter settings, we illustrate that exact graph matching runtime and also matchability are both functions of ϱT, with thresholding behavior starkly illustrated in matchability