  Several works based on Generative Adversarial Networks (GAN) have been
recently proposed to predict a set of medical images from a single modality
(e.g, FLAIR MRI from T1 MRI). However, such frameworks are primarily designed
to operate on images, limiting their generalizability to non-Euclidean
geometric data such as brain graphs. While a growing number of connectomic
studies has demonstrated the promise of including brain graphs for diagnosing
neurological disorders, no geometric deep learning work was designed for
multiple target brain graphs prediction from a source brain graph. Despite the
momentum the field of graph generation has gained in the last two years,
existing works have two critical drawbacks. First, the bulk of such works aims
to learn one model for each target domain to generate from a source domain.
Thus, they have a limited scalability in jointly predicting multiple target
domains. Second, they merely consider the global topological scale of a graph
(i.e., graph connectivity structure) and overlook the local topology at the
node scale of a graph (e.g., how central a node is in the graph). To meet these
challenges, we introduce MultiGraphGAN architecture, which not only predicts
multiple brain graphs from a single brain graph but also preserves the
topological structure of each target graph to predict. Its three core
contributions lie in: (i) designing a graph adversarial auto-encoder for
jointly predicting brain graphs from a single one, (ii) handling the mode
collapse problem of GAN by clustering the encoded source graphs and proposing a
cluster-specific decoder, (iii) introducing a topological loss to force the
reconstruction of topologically sound target brain graphs. Our MultiGraphGAN
significantly outperformed its variants thereby showing its great potential in
multi-view brain graph generation from a single graph.
