Most existing approaches of attributed network embedding often combine topology and attribute information based on the homophily assumption. In many real-world networks, such an assumption does not hold since the nodes are usually associated with many noisy or irrelevant attributes. To tackle this issue, we propose a noise-resistant graph embedding method, called NGE, by leveraging the subspace clustering information (i.e., the formation of communities is driven by different latent features in distinct subspaces). Specifically, we first construct a tensor to represent a given attributed network and then map it into different feature subspaces to capture community structure via tensor decomposition. For structure embedding, the link-level and community-level constraints are imposed. For attribute embedding, the feature-selection constraint is used to reinforce the relationship between topology and noise-removal attributes. By learning structure and attribute embedding with subspace clustering information, NGE can benefit both community detection, link prediction, and node classification. Extensive experimental results have demonstrated the superiority of NGE over many state-of-the-art approaches.

中文翻译：

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向具有子空间聚类信息的抗噪声图嵌入

大多数现有的属性网络嵌入方法通常基于同质假设结合拓扑和属性信息。在许多现实世界的网络中，这样的假设并不成立，因为节点通常与许多嘈杂或不相关的属性相关联。为了解决这个问题，我们通过利用子空间聚类信息（即社区的形成由不同子空间中的不同潜在特征驱动）提出了一种称为 NGE 的抗噪声图嵌入方法。具体来说，我们首先构造一个张量来表示给定的属性网络，然后将其映射到不同的特征子空间，以通过张量分解来捕获社区结构。对于结构嵌入，施加了链接级别和社区级别的约束。对于属性嵌入，特征选择约束用于加强拓扑和噪声去除属性之间的关系。通过使用子空间聚类信息学习结构和属性嵌入，NGE 可以有益于社区检测、链接预测和节点分类。广泛的实验结果证明了 NGE 优于许多最先进的方法。