  Dynamic community detection has been prospered as a powerful tool for
quantifying changes in dynamic brain network connectivity patterns by
identifying strongly connected sets of nodes. However, as the network science
problems and network data to be processed become gradually more sophisticated,
it awaits a better method to efficiently learn low dimensional representation
from dynamic network data and reveal its latent function that changes over time
in the brain network. In this work, an adversarial temporal graph
representation learning (ATGRL) framework is proposed to detect dynamic
communities from a small sample of brain network data. It adopts a novel
temporal graph attention network as an encoder to capture more efficient
spatio-temporal features by attention mechanism in both spatial and temporal
dimensions. In addition, the framework employs adversarial training to guide
the learning of temporal graph representation and optimize the measurable
modularity loss to maximize the modularity of community. Experiments on the
real-world brain networks datasets are demonstrated to show the effectiveness
of this new method.
