Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions, while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task.

中文翻译：

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使用循环神经网络生成基于边的序列图

使用机器学习生成图是一个在各个研究领域都有应用的开放性问题。在这项工作中，我们建议依靠节点排序过程将图的生成过程转换为连续的过程。我们使用这个顺序过程来设计一个新的生成模型，该模型由两个循环神经网络组成，这些网络学习预测图的边：第一个网络生成每条边的一个端点，而第二个网络生成另一个以图的状态为条件的端点。第一的。我们在五个不同的数据集上广泛测试我们的方法，与来自图文献的两个众所周知的基线和两种循环方法进行比较，其中一种具有最先进的性能。评估是在考虑生成样本的定量和定性特征的情况下进行的。结果表明，我们的方法能够产生源自非常不同的分布的新颖独特的图，同时保留与训练样本非常相似的结构特性。在提议的评估框架下，我们的方法能够在图形生成任务上达到与当前最先进水平相当的性能。