Matched Filters for Noisy Induced Subgraph Detection
This addresses a problem in social networks, neuroscience, and computer vision, but it is incremental as it builds on existing graph matching methods with centering and padding schemes.
The paper tackles the problem of finding vertex correspondence between two noisy graphs of different sizes, proposing a graph matching matched filter method that centers and pads the smaller adjacency matrix to align it with the larger network. Under a statistical model, this approach guarantees recovery of the true correspondence, and simulations on Drosophila and human connectomes show good performance.
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.