Contrastive learning methods have attracted considerable attention due to their remarkable success in analyzing graph-structured data. Inspired by the success of contrastive learning, we propose a novel framework for contrastive disentangled learning on graphs, employing a disentangled graph encoder and two carefully crafted self-supervision signals. Specifically, we introduce a disentangled graph encoder to enforce the framework to distinguish various latent factors corresponding to underlying semantic information and learn the disentangled node embeddings. Moreover, to overcome the heavy reliance on labels, we design two self-supervision signals, namely node specificity and channel independence, which capture informative knowledge without the need for labeled data, thereby guiding the automatic disentanglement of nodes. Finally, we perform node classification tasks on three citation networks by using the disentangled node embeddings, and the relevant analysis is provided. Experimental results validate the effectiveness of the proposed framework compared with various baselines.

翻译：对比学习方法因其在图结构数据分析中的显著成功而备受关注。受对比学习成功的启发，我们提出了一种新颖的图对比解耦学习框架，采用解构图编码器以及两个精心设计的自监督信号。具体而言，我们引入解构图编码器，强制框架区分与潜在语义信息相对应的各种潜在因子，并学习解耦的节点嵌入。此外，为了摆脱对标签的过度依赖，我们设计了两个自监督信号——节点特异性和通道独立性，这些信号无需标注数据即可捕获信息性知识，从而指导节点的自动解耦。最后，我们利用解耦节点嵌入在三个引文网络上执行节点分类任务，并提供了相关分析。实验结果验证了所提出框架与多种基线方法相比的有效性。