  Motifs, which have been established as building blocks for network structure,
move beyond pair-wise connections to capture longer-range correlations in
connections and activity. In spite of this, there are few generative graph
models that consider higher-order network structures and even fewer that focus
on using motifs in models of dynamic graphs. Most existing generative models
for temporal graphs strictly grow the networks via edge addition, and the
models are evaluated using static graph structure metrics -- which do not
adequately capture the temporal behavior of the network. To address these
issues, in this work we propose DYnamic MOtif-NoDes (DYMOND) -- a generative
model that considers (i) the dynamic changes in overall graph structure using
temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node
plays the hub role in a wedge, while the remaining two act as spokes). We
compare DYMOND to three dynamic graph generative model baselines on real-world
networks and show that DYMOND performs better at generating graph structure and
node behavior similar to the observed network. We also propose a new
methodology to adapt graph structure metrics to better evaluate the temporal
aspect of the network. These metrics take into account the changes in overall
graph structure and the individual nodes' behavior over time.
