The key benefit of the GraphRNN approach is that it models dependencies between edges. Using an autoregressive modeling assumption, GraphRNN can condition the generation of edges at generation step i based on the state of the graph that has already been generated during generation steps 1, ...i − 1, which makes it much easier to generate complex motifs and regular graph structures, such as grids.