A consistent scalable two-sample test for unlabeled networks based on spectral matching.

We establish the theoretical limits of clustering under the Popularity-Adjusted Block Model and show that even without edge-density signals, cluster recovery is possible by leveraging differences in intra- and inter-cluster popularity parameters

Graph convolutions on node features admit a natural central limit theorem which has consequences to multi-class node classification.