inexact-graph-matching-brain-networks

You can compare brain networks by computing an inexact graph edit distance, which quantifies the minimum-cost edits needed between two graphs. This approach handles uncertain node correspondences by outputting both a distance score and a node mapping.

Graph edit distance measures the minimum-cost edits required to transform one brain network graph into another, quantifying dissimilarity. In functional connectivity analysis, it helps assess variation, heritability, or disease-related changes across individuals.

Yes, you can apply inexact graph matching to pairwise comparisons of monozygotic versus dizygotic twin brain networks to study heritability. It calculates graph edit distances to quantify structural similarities and variations.

Brain networks must be provided as graphs where nodes represent brain regions and edges have weights indicating functional connectivity. These weighted graphs are required to compute the graph edit distance and generate node mappings.

Yes, inexact graph matching works with longitudinal datasets to track developmental changes over time. By computing graph edit distances between sequential brain networks, it quantifies variation and outputs node correspondence mappings.

Node correspondence mapping identifies the likely matching of brain regions between two graphs, helping interpret structural differences. It is output alongside the graph edit distance score to explain how individual brain regions vary.