Zhou Dawei, Zheng Lecheng, Xu Jiejun, He Jingrui
Arizona State University, Tempe, AZ, United States.
HRL Laboratories, LLC, Malibu, CA, United States.
Front Big Data. 2019 Apr 25;2:3. doi: 10.3389/fdata.2019.00003. eCollection 2019.
Characterizing and modeling the distribution of a particular family of graphs are essential for the studying real-world networks in a broad spectrum of disciplines, ranging from market-basket analysis to biology, from social science to neuroscience. However, it is unclear how to model these complex graph organizations and learn generative models from an observed graph. The key challenges stem from the non-unique, high-dimensional nature of graphs, as well as graph community structures at different granularity levels. In this paper, we propose a multi-scale graph generative model named , which models the underlying distribution of graph structures at different levels of granularity, and then "transfers" such hierarchical distribution from the graphs in the domain of interest, to a unique graph representation. The empirical results on seven real data sets demonstrate the effectiveness of the proposed framework.
表征和建模特定图族的分布对于在从市场篮子分析到生物学、从社会科学到神经科学等广泛学科领域中研究现实世界网络至关重要。然而，目前尚不清楚如何对这些复杂的图组织进行建模以及如何从观察到的图中学习生成模型。关键挑战源于图的非唯一、高维性质以及不同粒度级别的图社区结构。在本文中，我们提出了一种名为 的多尺度图生成模型，该模型对不同粒度级别下图结构的潜在分布进行建模，然后将这种层次分布从感兴趣领域的图“转移”到唯一的图表示。在七个真实数据集上的实证结果证明了所提出框架的有效性。
