Typicality Matching for Pairs of Correlated Graphs

TL;DR

This paper introduces a typicality matching algorithm for pairing correlated graphs with multi-valued edges, establishing conditions for successful matching based on mutual information, relevant in social, biological, and web data analysis.

Contribution

The paper proposes a novel typicality matching algorithm and derives an achievable region for graph matching based on mutual information between edge variables.

Findings

Achievable matching region depends on mutual information.

Algorithm investigates joint typicality of adjacency matrices.

Results applicable to social, biological, and web graphs.

Abstract

In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network de-anonymization, study of biological data, web graphs, etc. An achievable region for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching. The algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs. Our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs. The result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest.