  Quantifying the similarity between two networks is critical in many
applications. A number of algorithms have been proposed to compute graph
similarity, mainly based on the properties of nodes and edges. Interestingly,
most of these algorithms ignore the physical location of the nodes, which is a
key factor in the context of brain networks involving spatially defined
functional areas. In this paper, we present a novel algorithm called "SimiNet"
for measuring similarity between two graphs whose nodes are defined a priori
within a 3D coordinate system. SimiNet provides a quantified index (ranging
from 0 to 1) that accounts for node, edge and spatiality features. Complex
graphs were simulated to evaluate the performance of SimiNet that is compared
with eight state-of-art methods. Results show that SimiNet is able to detect
weak spatial variations in compared graphs in addition to computing similarity
using both nodes and edges. SimiNet was also applied to real brain networks
obtained during a visual recognition task. The algorithm shows high performance
to detect spatial variation of brain networks obtained during a naming task of
two categories of visual stimuli: animals and tools. A perspective to this work
is a better understanding of object categorization in the human brain.
