具有超边置换文法的生长图
Growing Graphs with Hyperedge Replacement Graph Grammars
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。在本文中，我们表明，一个图的团树可以用来提取一个超边替换文法。如果我们存储来自提取过程的排序，则保证提取的图语法生成原始图的同构副本。或者，可以使用图形语法规则的随机应用来快速创建随机图形。在大型现实世界的网络实验中，我们表明，随机图，从提取的图形语法生成，表现出广泛的性质，是非常相似的原始图形。除了度或特征向量中心性等图形属性之外，图形的“外观”最终取决于局部图形子结构中的小细节，这些细节难以在全局级别上定义。我们表明，我们的生成图模型是能够保留这些局部子结构时，生成新的图形和执行良好的新的和困难的模型鲁棒性测试。
英文摘要：
Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. In this paper we show that a graph's clique tree can be used to extract a hyperedge replacement grammar. If we store an ordering from the extraction process, the extracted graph grammar is guaranteed to generate an isomorphic copy of the original graph. Or, a stochastic application of the graph grammar rules can be used to quickly create random graphs. In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs. In addition to graph properties like degree or eigenvector centrality, what a graph "looks like" ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that our generative graph model is able to preserve these local substructures when generating new graphs and performs well on new and difficult tests of model robustness.
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。
