GraphRNN Revisited: An Ablation Study and Extensions for Directed
  Acyclic Graphs
Taniya Das Mark Koch Maya Ravichandran Nikhil Khatri
GraphRNN is a deep learning-based architecture proposed by You et al. for
learning generative models for graphs. We replicate the results of You et al.
using a reproduced implementation of the GraphRNN architecture and evaluate
this against baseline models using new metrics. Through an ablation study, we
find that the BFS traversal suggested by You et al. to collapse representations
of isomorphic graphs contributes significantly to model performance.
Additionally, we extend GraphRNN to generate directed acyclic graphs by
replacing the BFS traversal with a topological sort. We demonstrate that this
method improves significantly over a directed-multiclass variant of GraphRNN on
a real-world dataset.