TD-GEN：用树分解生成图
TD-GEN: Graph Generation With Tree Decomposition
摘要：
我们提出了TD-GEN，一个基于树分解的图生成框架，并引入了一个减少的上界图生成所需的决策的最大数量。该框架包括一个置换不变树生成模型，形成了图形生成的骨干。树节点是超级节点，每个超级节点代表图中的一个节点集群。图节点和边通过遍历树超节点、尊重树分解的结构以及遵循集群之间的节点共享决策而在集群内部增量地生成。最后，我们讨论了标准的评价标准的基础上生成的图形作为性能指标的统计特性的缺点。我们建议基于似然性来比较模型的性能。在各种标准图生成数据集上的实验结果证明了该方法的优越性能。
英文摘要：
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation. The framework includes a permutation invariant tree generation model which forms the backbone of graph generation. Tree nodes are supernodes, each representing a cluster of nodes in the graph. Graph nodes and edges are incrementally generated inside the clusters by traversing the tree supernodes, respecting the structure of the tree decomposition, and following node sharing decisions between the clusters. Finally, we discuss the shortcomings of standard evaluation criteria based on statistical properties of the generated graphs as performance measures. We propose to compare the performance of models based on likelihood. Empirical results on a variety of standard graph generation datasets demonstrate the superior performance of our method.
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