Matched Filters for Noisy Induced Subgraph Detection.

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 cur

SUBMITTER: Sussman DL 

PROVIDER: S-EPMC7598933 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature