生成时态图的时态树分解
A Temporal Tree Decomposition for Generating Temporal Graphs
摘要：
发现存在于大型真实世界图中的底层结构是一个基本的科学问题。最近的工作在形式语言理论和图论的交叉发现，超边替换语法（HRG）可以从任何图的树分解提取。该HRG可用于生成共享与原始图相似的属性的新图。因为提取的HRG直接依赖于树分解的形状和内容，所以不太可能用提取算法实际捕获信息图形过程。为了解决这个问题，目前的工作提出了一种新的提取算法，称为时间HRG（tHRG），学习HRG产生式规则从时间树分解的图形。我们观察到的问题，在一个时间HRG模型的假设。在大型真实世界网络的实验中，我们展示并提供了为什么tHRG不执行以及HRG和其他图形生成器的推理。
英文摘要：
Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. Recent work at the intersection of formal language theory and graph theory has found that a Hyperedge Replacement Grammar (HRG) can be extracted from a tree decomposition of any graph. This HRG can be used to generate new graphs that share properties that are similar to the original graph. Because the extracted HRG is directly dependent on the shape and contents of the of tree decomposition, it is unlikely that informative graph-processes are actually being captured with the extraction algorithm. To address this problem, the current work presents a new extraction algorithm called temporal HRG (tHRG) that learns HRG production rules from a temporal tree decomposition of the graph. We observe problems with the assumptions that are made in a temporal HRG model. In experiments on large real world networks, we show and provide reasoning as to why tHRG does not perform as well as HRG and other graph generators.
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。
