  Graphs are a natural representation of brain activity derived from functional
magnetic imaging (fMRI) data. It is well known that clusters of anatomical
brain regions, known as functional connectivity networks (FCNs), encode
temporal relationships which can serve as useful biomarkers for understanding
brain function and dysfunction. Previous works, however, ignore the temporal
dynamics of the brain and focus on static graphs. In this paper, we propose a
dynamic brain graph deep generative model (DBGDGM) which simultaneously
clusters brain regions into temporally evolving communities and learns dynamic
unsupervised node embeddings. Specifically, DBGDGM represents brain graph nodes
as embeddings sampled from a distribution over communities that evolve over
time. We parameterise this community distribution using neural networks that
learn from subject and node embeddings as well as past community assignments.
Experiments demonstrate DBGDGM outperforms baselines in graph generation,
dynamic link prediction, and is comparable for graph classification. Finally,
an analysis of the learnt community distributions reveals overlap with known
FCNs reported in neuroscience literature.
