Graph theory has drawn a lot of attention in the field of Neuroscience during
the last decade, mainly due to the abundance of tools that it provides to
explore the interactions of elements in a complex network like the brain. The
local and global organization of a brain network can shed light on mechanisms
of complex cognitive functions, while disruptions within the network can be
linked to neurodevelopmental disorders. In this effort, the construction of a
representative brain network for each individual is critical for further
analysis. Additionally, graph comparison is an essential step for inference and
classification analyses on brain graphs. In this work we explore a method based
on graph edit distance for evaluating graph similarity, when correspondences
between network elements are unknown due to different underlying subdivisions
of the brain. We test this method on 30 unrelated subjects as well as 40 twin
pairs and show that this method can accurately reflect the higher similarity
between two related networks compared to unrelated ones, while identifying node
correspondences.