Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.

中文翻译：

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struc2gauss：通过高斯嵌入来保持结构性角色的网络嵌入

由于网络嵌入（NE）能够将节点映射到有效的低维嵌入向量中，因此在网络挖掘中起着主要作用。但是，最新的NE方法存在两个主要限制：角色保留和不确定性建模。几乎所有先前的方法都将节点表示为空间中的一个点，并专注于局部结构信息（即邻域信息）。但是，邻域信息无法捕获全局结构信息，并且点矢量表示无法对节点表示的不确定性进行建模。在本文中，我们提出了一个新的NE框架struc2gauss，该算法学习高斯分布空间中的节点表示形式，并根据全局结构信息执行网络嵌入。struc2gauss首先使用给定的节点相似性度量来度量全局结构信息，然后为节点生成结构上下文，最后通过高斯嵌入学习节点表示形式。研究了网络的不同结构相似性度量和高斯嵌入的能量函数。在实际网络上进行的实验表明，struc2gauss 有效地捕获全局结构信息，而最先进的网络嵌入方法却无法胜任，在基于结构的聚类和分类任务上胜过其他方法，并提供有关节点表示不确定性的更多信息。