The key benefit of the GRAN model, compared to GraphRNN, is that it does not need to maintain a long and complex history in a graph-level RNN. Instead, the GRAN model explicitly conditions on the already generated graph using a GNN at each generation step.

Hamilton College

To address some of the limitations of the GraphRNN approach, GRAN model was proposed. GRAN maintains the autoregressive decomposition of the generation process, and instead of using RNNs to model the autoregressive generation process, GRAN uses GNNs. The key idea in GRAN is that we can model the conditional distribution of each row of the adjacency matrix by running a GNN on the graph that has been generated so far.

Graph Recurrent Attention Networks (GRAN)

Hamilton, W.L. Graph Representation Learning. *Synthesis Lectures on Artificial Intelligence and Machine Learning*. 2020, 14(3), 1–159. https://doi.org/10.2200/S01045ED1V01Y202009AIM046

Graph Representation Learning by William Hamilton

Key Benefit of the GRAN Model

The GRAN model can be trained in an analogous manner as GraphRNN
by maximizing the likelihood of training graphs using teacher forcing. Like the GraphRNN model, we must also specify an ordering over nodes to compute the likelihood on training graphs. Lastly, like the GraphRNN model, we can use GRAN as a generative model after training simply by running the stochastic generation process.

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