Alignment strength and correlation for graphs

Authors

Donniell E. Fishkind, Johns Hopkins University
Lingyao Meng, Johns Hopkins University
Ao Sun, Johns Hopkins University
Carey E. Priebe, Johns Hopkins University
Vince Lyzinski, University of Massachusetts Amherst

Publication Date

2019

Journal or Book Title

Pattern Recognition Letters

Abstract

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 ϱ_T 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 ϱ_T, with thresholding behavior starkly illustrated in matchability.

DOI

https://doi.org/10.1016/j.patrec.2019.05.008

Pages

295-302

Volume

125

License

UMass Amherst Open Access Policy

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Recommended Citation

Fishkind, Donniell E.; Meng, Lingyao; Sun, Ao; Priebe, Carey E.; and Lyzinski, Vince, "Alignment strength and correlation for graphs" (2019). Pattern Recognition Letters. 1295.
https://doi.org/10.1016/j.patrec.2019.05.008