Network representation learning called NRL for short aims at embedding various networks into low-dimensional continuous distributed vector spaces. Most existing representation learning methods focus on learning representations purely based on the network topology, i.e., the linkage relationships between network nodes, but the nodes in lots of networks may contain rich text features, which are beneficial to network analysis tasks, such as node classification, link prediction and so on. In this paper, we propose a novel network representation learning model, which is named as Text-Enhanced Network Representation Learning called TENR for short, by introducing text features of the nodes to learn more discriminative network representations, which come from joint learning of both the network topology and text features, and include common influencing factors of both parties. In the experiments, we evaluate our proposed method and other baseline methods on the task of node classification. The experimental results demonstrate that our method outperforms other baseline methods on three real-world datasets.

中文翻译：

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文本增强的网络表示学习

简称为NRL的网络表示学习旨在将各种网络嵌入到低维连续分布向量空间中。大多数现有的表示学习方法都集中于纯粹基于网络拓扑来学习表示，即基于网络节点之间的链接关系，但是许多网络中的节点可能包含丰富的文本特征，这有利于网络分析任务（例如节点分类） ，链接预测等。在本文中，我们通过引入节点的文本特征来学习更多的判别性网络表示，从而提出了一种新颖的网络表示学习模型，该模型称为文本增强网络表示学习（简称TENR），简称TENR。网络拓扑和文本功能，并包括双方共同的影响因素。在实验中，我们对节点分类任务评估了我们提出的方法和其他基线方法。实验结果表明，在三个真实数据集上，我们的方法优于其他基准方法。