Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information. Current representation learning methods for temporal networks often focus on capturing fine-grained information, which may lead to the model capturing random noise instead of essential semantic information. While graph contrastive learning has shown promise in dealing with noise, it only applies to static graphs or snapshots and may not be suitable for handling time-dependent noise. To alleviate the above challenge, we propose a novel Temporal Graph representation learning with Adaptive augmentation Contrastive (TGAC) model. The adaptive augmentation on the temporal graph is made by combining prior knowledge with temporal information, and the contrastive objective function is constructed by defining the augmented inter-view contrast and intra-view contrast. To complement TGAC, we propose three adaptive augmentation strategies that modify topological features to reduce noise from the network. Our extensive experiments on various real networks demonstrate that the proposed model outperforms other temporal graph representation learning methods.

翻译：时间图表示学习旨在生成低维动态节点嵌入，以捕获时间信息以及结构和属性信息。当前针对时间网络的表示学习方法通常侧重于捕捉细粒度信息，这可能导致模型捕获随机噪声而非本质语义信息。尽管图对比学习在处理噪声方面展现出潜力，但其仅适用于静态图或快照，可能无法处理时间依赖性噪声。为缓解上述挑战，我们提出了一种新颖的基于自适应增强对比的时间图表示学习模型（TGAC）。该模型通过将先验知识与时间信息相结合来实现时间图的自适应增强，并通过定义增强后的视图间对比和视图内对比来构建对比目标函数。为补充TGAC，我们提出了三种自适应增强策略，通过修改拓扑特征来减少网络中的噪声。在多种真实网络上的大量实验表明，所提模型优于其他时间图表示学习方法。