SimNet: A new algorithm for measuring brain networks similarity
Measuring similarity among graphs is recognized as a nontrivial problem. Most of the algorithms proposed so far ignore the spatial location of vertices, which is a crucial factor in the context of brain networks. In this paper, we present a novel algorithm, called “SimNet”, for measuring the similarity between two graphs whose vertices represent the position of sources over the cortex. The novelty is to account for differences at the level of spatiallyregistered vertices and edges. Simulated graphs are used to evaluate the algorithm performance and to compare it with methods reported elsewhere. Results show that SimNet is able to quantify the similarity between two graphs under a spatial constraint based on the 3D location of edges. The application of SimNet on real data (dense EEG) reveals the presence of spatiallydifferent brain networks modules activating during cognitive activity.
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Bibtex@inproceedings{MheHasWenKhaDufGriBer2015,
author = {A. Mheich and M. Hassan and F. Wendling
and M. Khalil and O. Dufor and V. Gripon and C.
Berrou},
title = {SimNet: A new algorithm for measuring brain
networks similarity},
booktitle = {Proceedings of the ICABME international
conference},
year = {2015},
pages = {119122},
}


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