论文标题
对图生成深的生成模型的系统调查
A Systematic Survey on Deep Generative Models for Graph Generation
论文作者
论文摘要
图是描述对象及其关系的重要数据表示，这些数据表示出现在各种现实世界中。作为该领域的关键问题之一，图表生成考虑了学习给定图的分布并生成更多新颖的图。然而，由于它们的广泛应用，图形的生成模型具有丰富的历史，传统上是手工制作的，并且只能对图的一些统计属性进行建模。深度生成模型的最新进展是改善生成图的忠诚度的重要一步，并为新的应用程序铺平了道路。本文提供了有关图形生成的深层生成模型领域文献的广泛概述。首先，提供了图形生成和初步知识的深层生成模型的形式定义。其次，分别提出了针对无条件和条件图生成的深层生成模型的分类学。比较和分析每个作品的现有作品。之后，提供了该特定领域中评估指标的概述。最后，总结了深图生成启用的应用程序，并突出了五个有希望的未来研究方向。
Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to their wide range of applications, generative models for graphs, which have a rich history, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation and the preliminary knowledge are provided. Secondly, taxonomies of deep generative models for both unconditional and conditional graph generation are proposed respectively; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.
