  With the recent technological advances, biological datasets, often
represented by networks (i.e., graphs) of interacting entities, proliferate
with unprecedented complexity and heterogeneity. Although modern network
science opens new frontiers of analyzing connectivity patterns in such
datasets, we still lack data-driven methods for extracting an integral
connectional fingerprint of a multi-view graph population, let alone
disentangling the typical from the atypical variations across the population
samples. We present the multi-view graph normalizer network (MGN-Net;
https://github.com/basiralab/MGN-Net), a graph neural network based method to
normalize and integrate a set of multi-view biological networks into a single
connectional template that is centered, representative, and topologically
sound. We demonstrate the use of MGN-Net by discovering the connectional
fingerprints of healthy and neurologically disordered brain network populations
including Alzheimer's disease and Autism spectrum disorder patients.
Additionally, by comparing the learned templates of healthy and disordered
populations, we show that MGN-Net significantly outperforms conventional
network integration methods across extensive experiments in terms of producing
the most centered templates, recapitulating unique traits of populations, and
preserving the complex topology of biological networks. Our evaluations showed
that MGN-Net is powerfully generic and easily adaptable in design to different
graph-based problems such as identification of relevant connections,
normalization and integration.
