Network embedding, which targets at learning the vector representation of vertices, has become a crucial issue in network analysis. However, considering the complex structures and heterogeneous attributes in real-world networks, existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity. Thus, more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information. To that end, in this paper, we propose a heterogeneous-attributes enhancement deep framework (HEDF), which could better capture the non-linear structure and associated information in a deep learning way, and effectively combine the structure information of multi-views by the combining layer. Along this line, the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode. The extensive validations on several real-world datasets show that our model could outperform the baselines, especially for the sparse and inconsistent situation with less training data.

以学习顶点的向量表示为目标的网络嵌入已成为网络分析中的一个关键问题。然而，考虑到现实世界网络中的复杂结构和异构属性，现有方法可能无法处理结构拓扑和属性邻近性之间的不一致。因此，迫切需要更全面的技术来捕捉高度非线性的网络结构并解决现有的不一致问题，同时保留更多信息。为此，在本文中，我们提出了一种异构属性增强深度框架（HEDF），它可以更好地以深度学习的方式捕捉非线性结构和相关信息，并通过以下方式有效地结合多视图的结构信息结合层。沿着这条线，不一致性会得到一定程度的处理，更多的结构信息会通过半监督的方式得到保留。对几个真实世界数据集的广泛验证表明，我们的模型可以胜过基线，特别是对于训练数据较少的稀疏和不一致的情况。