论文标题
deepgg：深图生成器
DeepGG: a Deep Graph Generator
论文作者
论文摘要
图形的学习分布可用于自动药物发现，分子设计，复杂的网络分析等。我们提出了一个改进的框架，以根据深层机器的思想来学习图形的生成模型。为了学习状态过渡决策，我们使用一组图形和节点嵌入技术作为状态计算机的内存。 我们的分析基于学习随机图生成器的分布，我们为其提供统计测试以确定可以学习哪些属性以及图表的原始分布的表现。我们表明，状态机的设计有利于特定的分布。尺寸的模型最多可以学习150个顶点。代码和参数可公开复制我们的结果。
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more. We present an improved framework for learning generative models of graphs based on the idea of deep state machines. To learn state transition decisions we use a set of graph and node embedding techniques as memory of the state machine. Our analysis is based on learning the distribution of random graph generators for which we provide statistical tests to determine which properties can be learned and how well the original distribution of graphs is represented. We show that the design of the state machine favors specific distributions. Models of graphs of size up to 150 vertices are learned. Code and parameters are publicly available to reproduce our results.
