Matched Filters for Noisy Induced Subgraph Detection

TL;DR

This paper introduces a graph matching filter method for identifying vertex correspondence between noisy, differently sized graphs, with theoretical guarantees and practical demonstrations on biological networks.

Contribution

It proposes a novel graph matching approach using centering and padding, applicable to any adjacency matrix matching algorithm, with theoretical recovery guarantees under a statistical model.

Findings

Method achieves good performance on biological network data.

The approach guarantees true correspondence recovery under certain models.

Demonstrates the method's applicability to real-world noisy graph data.

Abstract

The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to this problem via a graph matching matched filter: centering and padding the smaller adjacency matrix and applying graph matching methods to align it to the larger network. The centering and padding schemes can be incorporated into any algorithm that matches using adjacency matrices. Under a statistical model for correlated pairs of graphs, which yields a noisy copy of the small graph within the larger graph, the resulting optimization problem can be guaranteed to recover the true vertex correspondence between the networks. However, there are currently no efficient algorithms for solving this problem. To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to {\it Drosophila} and human connectomes that this approach can achieve good performance.