  Detecting anomalous behavior in dynamic networks remains a constant
challenge. This problem is further exacerbated when the underlying topology of
these networks is affected by individual highly-dimensional node attributes. We
address this issue by tracking a network's modularity as a proxy of its
community structure. We leverage Graph Neural Networks (GNNs) to estimate each
snapshot's modularity. GNNs can account for both network structure and
high-dimensional node attributes, providing a comprehensive approach for
estimating network statistics. Our method is validated through simulations that
demonstrate its ability to detect changes in highly-attributed networks by
analyzing shifts in modularity. Moreover, we find our method is able to detect
a real-world event within the \#Iran Twitter reply network, where each node has
high-dimensional textual attributes.
