Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive embodiment of both the intrinsic stable characteristics of nodes and the time-related dynamic preference. However, existing methods generally mix these two types of information into a single representation space, which may lead to poor explanation, less robustness, and a limited ability when applied to different downstream tasks. To solve the above problems, in this paper, we propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively. To further enhance the disentanglement of these two types of representation, we propose a disentanglement-aware discriminator under an adversarial learning framework from the perspective of information theory. Extensive experiments on Tencent and five commonly used public datasets demonstrate that DyTed, as a general framework that can be applied to existing methods, achieves state-of-the-art performance on various downstream tasks, as well as be more robust against noise.

翻译：近年来，无监督动态图表示学习引起了研究界的广泛关注。与静态图相比，动态图是节点内在稳定特征与时间相关动态偏好的综合体现。然而，现有方法通常将这两类信息混合在单一表示空间中，这可能导致解释性差、鲁棒性不足，并限制其在不同下游任务中的适用性。为解决上述问题，本文提出了一种新颖的离散时间动态图解耦表示学习框架——DyTed。我们特别设计了时间片段对比学习任务与结构对比学习任务，分别有效识别时不变与时变表示。为进一步增强这两类表示的解耦效果，我们基于信息论视角，在对抗学习框架下提出了一种解耦感知判别器。在腾讯数据集及五个常用公开数据集上的大量实验表明，DyTed作为一种可应用于现有方法的通用框架，在多种下游任务上取得了最先进的性能，并且对噪声具有更强的鲁棒性。