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.

通过复制 You et al. 的 GraphRNN 架构，我们评估了重现实现的 GraphRNN 架构与基准模型的新指标，并通过消融研究发现，You et al. 建议的 BFS 遍历在同构图的表示折叠中起到了重要作用。此外，我们通过使用拓扑排序替代 BFS 遍历，将 GraphRNN 扩展为生成有向无环图，证明这种方法在实际数据集上明显改进了 GraphRNN 的多类别有向变体。

GraphRNN 再探：有向无环图的剖析研究与扩展

GraphRNN Revisited: An Ablation Study and Extensions for Directed  Acyclic Graphs

Modeling and generating graphs is fundamental for studying networks in
biology, engineering, and social sciences. However, modeling complex
distributions over graphs and then efficiently sampling from these
distributions is challenging due to the non-unique, high-dimensional nature of
graphs and the complex, non-local dependencies that exist between edges in a
given graph. Here we propose GraphRNN, a deep autoregressive model that
addresses the above challenges and approximates any distribution of graphs with
minimal assumptions about their structure. GraphRNN learns to generate graphs
by training on a representative set of graphs and decomposes the graph
generation process into a sequence of node and edge formations, conditioned on
the graph structure generated so far.
In order to quantitatively evaluate the performance of GraphRNN, we introduce
a benchmark suite of datasets, baselines and novel evaluation metrics based on
Maximum Mean Discrepancy, which measure distances between sets of graphs. Our
experiments show that GraphRNN significantly outperforms all baselines,
learning to generate diverse graphs that match the structural characteristics
of a target set, while also scaling to graphs 50 times larger than previous
deep models.

GraphRNN 是一种基于自回归模型的图生成模型，可以在没有先验结构假设的情况下模拟任何图分布，并且可以比现有的深度模型扩展到 50 倍以上规模的生成，而引入的基准测试集和最大平均差异评估方法可以有效评估模型表现。