Edges in many real-world social/information networks are associated with rich
text information (e.g., user-user communications or user-product reviews).
However, mainstream network representation learning models focus on propagating
and aggregating node attributes, lacking specific designs to utilize text
semantics on edges. While there exist edge-aware graph neural networks, they
directly initialize edge attributes as a feature vector, which cannot fully
capture the contextualized text semantics of edges. In this paper, we propose
Edgeformers, a framework built upon graph-enhanced Transformers, to perform
edge and node representation learning by modeling texts on edges in a
contextualized way. Specifically, in edge representation learning, we inject
network information into each Transformer layer when encoding edge texts; in
node representation learning, we aggregate edge representations through an
attention mechanism within each node's ego-graph. On five public datasets from
three different domains, Edgeformers consistently outperform state-of-the-art
baselines in edge classification and link prediction, demonstrating the
efficacy in learning edge and node representations, respectively.

Edgeformers 是建立在图增强 Transformer 之上的框架，通过对边缘文本进行建模来执行边缘和节点表示学习。在五个公共数据集中，Edgeformers 在边缘分类和链接预测方面始终优于最先进的基线，显示了学习边和节点表示的效力。