The computation of distance measures between nodes in graphs is inefficient
and does not scale to large graphs. We explore dense vector representations as
an effective way to approximate the same information: we introduce a simple yet
efficient and effective approach for learning graph embeddings. Instead of
directly operating on the graph structure, our method takes structural measures
of pairwise node similarities into account and learns dense node
representations reflecting user-defined graph distance measures, such as
e.g.the shortest path distance or distance measures that take information
beyond the graph structure into account. We demonstrate a speed-up of several
orders of magnitude when predicting word similarity by vector operations on our
embeddings as opposed to directly computing the respective path-based measures,
while outperforming various other graph embeddings on semantic similarity and
word sense disambiguation tasks and show evaluations on the WordNet graph and
two knowledge base graphs.

本文介绍一种学习图嵌入的简单、有效方法，通过密集向量表示来近似节点之间的距离，以反映用户定义的图距离度量，避免了直接在图结构上进行操作引起的低效性，证明该方法在语义相似性和词义消歧任务上的表现比其他图嵌入方法更优秀，同时在 WordNet 和两个知识库图上进行了评估。