Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. We propose to incorporate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. We introduce a word-by-word alignment framework that measures the compatibility of embeddings between word pairs, and then adaptively accumulates these alignment features with a simple yet effective aggregation function. In experiments, we evaluate the proposed framework on three real-world benchmarks for downstream tasks, including link prediction and multi-label vertex classification. Results demonstrate that our model outperforms state-of-the-art network embedding methods by a large margin.

该研究利用语义特征来匹配所有节点对的文本序列，提出了一种单词对齐框架，并通过简单有效的聚合函数积累对齐特征。实验发现，该模型在链接预测和多标签节点分类等下游任务中具有很大的优越性。

基于细粒度词对齐的改进语义感知网络嵌入