Edges in real-world graphs are typically formed by a variety of factors and
carry diverse relation semantics. For example, connections in a social network
could indicate friendship, being colleagues, or living in the same
neighborhood. However, these latent factors are usually concealed behind mere
edge existence due to the data collection and graph formation processes.
Despite rapid developments in graph learning over these years, most models take
a holistic approach and treat all edges as equal. One major difficulty in
disentangling edges is the lack of explicit supervisions. In this work, with
close examination of edge patterns, we propose three heuristics and design
three corresponding pretext tasks to guide the automatic edge disentanglement.
Concretely, these self-supervision tasks are enforced on a designed edge
disentanglement module to be trained jointly with the downstream node
classification task to encourage automatic edge disentanglement. Channels of
the disentanglement module are expected to capture distinguishable relations
and neighborhood interactions, and outputs from them are aggregated as node
representations. The proposed DisGNN is easy to be incorporated with various
neural architectures, and we conduct experiments on $6$ real-world datasets.
Empirical results show that it can achieve significant performance gains.

本文提出了一种自我监督方法并设计了三个前置任务来指导自动边界分离，同时将其与下游节点分类任务一起训练，以促进通道的捕获可区分关系和邻域相互作用，并且输出它们作为节点表示。实验结果表明 DisGNN 在六个真实世界数据集上取得了显着的性能提升，为边缘分离和图学习提供了一种新的解决方案。