GraphGen-Redux：一个快速轻量级的标号图递归生成模型
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation
摘要：
标记图生成的问题在深度学习社区中越来越受到关注。由于图空间的稀疏性和离散性，这项任务具有挑战性。在文献中已经提出了几种方法，其中大多数需要将图转换为编码其结构和标签的序列，并通过自回归生成模型来学习这些序列的分布。在这一系列的方法中，我们专注于GraphGen模型。GraphGen的预处理阶段将图转换为称为深度优先搜索（DFS）代码的唯一边序列，这样两个同构图就会被分配相同的DFS代码。DFS代码的每个元素与图边相关联：具体地，它是一个五元组，包括两个端点中的每一个的一个节点标识符、它们的节点标签和边标签。GraphGen学习自动回归地生成这样的序列，并独立地对五元组的每个分量的概率进行建模。虽然有效，但该模型所做的独立性假设过于宽松，无法精确地捕捉真实世界图的复杂标签依赖关系。通过引入一种新的图预处理方法，我们能够处理节点和边的标记信息。相应的模型，我们称之为GraphGen-Redux，改进了GraphGen在广泛的化学和社会图数据集中的生成性能。此外，它使用的参数比vanilla变体少约78%，平均需要的训练次数少50%。
英文摘要：
The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several approaches have been proposed in the literature, most of which require to transform the graphs into sequences that encode their structure and labels and to learn the distribution of such sequences through an auto-regressive generative model. Among this family of approaches, we focus on the GraphGen model. The preprocessing phase of GraphGen transforms graphs into unique edge sequences called Depth-First Search (DFS) codes, such that two isomorphic graphs are assigned the same DFS code. Each element of a DFS code is associated with a graph edge: specifically, it is a quintuple comprising one node identifier for each of the two endpoints, their node labels, and the edge label. GraphGen learns to generate such sequences auto-regressively and models the probability of each component of the quintuple independently. While effective, the independence assumption made by the model is too loose to capture the complex label dependencies of real-world graphs precisely. By introducing a novel graph preprocessing approach, we are able to process the labeling information of both nodes and edges jointly. The corresponding model, which we term GraphGen-Redux, improves upon the generative performances of GraphGen in a wide range of datasets of chemical and social graphs. In addition, it uses approximately 78% fewer parameters than the vanilla variant and requires 50% fewer epochs of training on average.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
