Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graphs, dynamic graphs are the integrative reflection of both the temporal-invariant or stable characteristics of nodes and the dynamic-fluctuate preference changing with time. However, existing dynamic graph representation learning methods generally confound these two types of information into a shared representation space, which may lead to poor explanation, less robustness, and a limited ability when applied to different downstream tasks. Taking the real dynamic graphs of daily capital transactions on Tencent as an example, the learned representation of the state-of-the-art method achieves only 32% accuracy in predicting temporal-invariant characteristics of users like annual income. In this paper, we introduce a novel temporal invariance-fluctuation disentangled representation learning framework for dynamic graphs, namely DyTed. In particular, we propose a temporal-invariant representation generator and a dynamic-fluctuate representation generator with carefully designed pretext tasks to identify the two types of representations in dynamic graphs. To further enhance the disentanglement or separation, we propose a disentanglement-aware discriminator under an adversarial learning framework. Extensive experiments on Tencent and five commonly used public datasets demonstrate that the different parts of our disentangled representation can achieve state-of-the-art performance on various downstream tasks, as well as be more robust against noise, and is a general framework that can further improve existing methods.
翻译:对动态图表进行不受监督的代表性学习近年来引起了许多研究关注。与静态图表相比,动态图表综合反映了节点的时间变化或稳定特点,以及随着时间的变化而变化的动态变化偏好。然而,现有的动态图表代表性学习方法通常将这两种类型的信息混为一个共享的展示空间,这可能导致解释不周、不那么稳和在应用不同的下游任务时能力有限。以Tencent每天资本交易的真实动态图表为例,先进方法在预测年收入等用户的时间变化或稳定特点方面只达到32%的准确度。在本文件中,我们为动态图表引入了一种全新的时间变化变化-变异、不相交错的表述学习框架,即DyTed。特别是,我们建议使用一个时间变化的表达生成器和一个动态变异的表述器,其精心设计的借口任务是在动态图表中辨别两种类型的表达方式。为了进一步强化混乱或分离,我们提议在动态图表中采用一种共同的对比性框架,即用一种共同的变动式的演示式框架,即用一种共同的变动式的演示式的演示式框架,以演示式式的演示式的演示式框架之下,作为一种共同的变式的对式的演示式的演示式的演示式框架。