Network representation learning (NRL) maps vertices into latent vector space for further network inference. The existing algorithms concern more about whether the vectors of two similar nodes be close in latent vector space while the hierarchy proximity has been largely neglected by them. The distribution of the representation vectors needs to reflect the hierarchical structural properties which widely exist in networks. In this paper, we propose a novel network representation learning framework that can encode the interpretable hierarchical structural semantics into the representation vectors. Specifically, we measure the distance and importance degree of nodes in the original network and map the nodes to a tree space. This makes the hierarchical structural relations in the original network be clearly revealed by the tree which is also of good interpretability. In this paper, the local structural proximities and the interpretable hierarchy knowledge are encoded into vector space by optimizing the objective function. Extensive experiments conducted on the realistic data sets demonstrate that the proposed approach outperforms the existing state-of-the-art approaches on tasks of node classification, link prediction, and visualization. Finally, a case study is conducted for further analysis about how the proposed model works.

网络表示学习（NRL）将顶点映射到潜在向量空间中，以进行进一步的网络推理。现有算法更多地关注两个相似节点的向量在潜在向量空间中是否接近，而层次结构的邻近性却被它们忽略了。表示向量的分布需要反映网络中广泛存在的分层结构特性。在本文中，我们提出了一种新颖的网络表示学习框架，该框架可以将可解释的分层结构语义编码为表示向量。具体来说，我们测量原始网络中节点的距离和重要性程度，并将节点映射到树空间。这使得树可以清楚地显示原始网络中的层次结构关系，这也具有良好的可解释性。本文通过优化目标函数将局部结构邻近性和可解释的层次知识编码到向量空间中。在实际数据集上进行的大量实验表明，该方法在节点分类，链接预测和可视化任务方面优于现有的最新方法。最后，进行了一个案例研究，以进一步分析所提出模型的工作原理。在实际数据集上进行的大量实验表明，该方法在节点分类，链接预测和可视化等任务方面优于现有的最新方法。最后，进行了一个案例研究，以进一步分析所提出模型的工作原理。在实际数据集上进行的大量实验表明，该方法在节点分类，链接预测和可视化任务方面优于现有的最新方法。最后，进行了一个案例研究，以进一步分析所提出模型的工作原理。