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Learning Hyperedge Replacement Grammars for Graph Generation
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2018-03-01 , DOI: 10.1109/tpami.2018.2810877 Salvador Aguinaga , David Chiang , Tim Weninger
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2018-03-01 , DOI: 10.1109/tpami.2018.2810877 Salvador Aguinaga , David Chiang , Tim Weninger
The discovery and analysis of network patterns are central to the scientific enterprise. In the present work, we developed and evaluated a new approach that learns the building blocks of graphs that can be used to understand and generate new realistic graphs. Our key insight is that a graph's clique tree encodes robust and precise information. We show that a Hyperedge Replacement Grammar (HRG) can be extracted from the clique tree, and we develop a fixed-size graph generation algorithm that can be used to produce new graphs of a specified size. In experiments on large real-world graphs, we show that graphs generated from the HRG approach exhibit a diverse range of properties that are similar to those found in the original networks. 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 the HRG model can also preserve these local substructures when generating new graphs.
中文翻译:
学习Hyperedge替换文法以生成图
网络模式的发现和分析对于科学企业至关重要。在当前的工作中,我们开发并评估了一种新的方法,该方法可学习可用于理解和生成新的逼真的图的图的构建块。我们的主要见识在于,图形的集团树可编码鲁棒而精确的信息。我们展示了可以从集团树中提取Hyperedge替换语法(HRG),并且我们开发了一种固定大小的图生成算法,该算法可用于生成指定大小的新图。在大型现实世界图上进行的实验中,我们显示了从HRG方法生成的图具有与原始网络中相似的多种属性。除了度数或特征向量中心度等图属性外,图的“外观”最终取决于难以在全局级别定义的局部图子结构中的小细节。我们表明,HRG模型在生成新图时还可以保留这些局部子结构。
更新日期:2019-02-06
中文翻译:
学习Hyperedge替换文法以生成图
网络模式的发现和分析对于科学企业至关重要。在当前的工作中,我们开发并评估了一种新的方法,该方法可学习可用于理解和生成新的逼真的图的图的构建块。我们的主要见识在于,图形的集团树可编码鲁棒而精确的信息。我们展示了可以从集团树中提取Hyperedge替换语法(HRG),并且我们开发了一种固定大小的图生成算法,该算法可用于生成指定大小的新图。在大型现实世界图上进行的实验中,我们显示了从HRG方法生成的图具有与原始网络中相似的多种属性。除了度数或特征向量中心度等图属性外,图的“外观”最终取决于难以在全局级别定义的局部图子结构中的小细节。我们表明,HRG模型在生成新图时还可以保留这些局部子结构。