Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline.

翻译：时序图网络（TGN）在学习连续时间动态图的表示方面表现出卓越性能。然而，现实世界的动态图通常包含多样且复杂的噪声。噪声会显著降低表示生成的质量，阻碍TGN在下游任务中的有效性。尽管结构学习被广泛应用于静态图以减轻噪声，但其在动态图场景中的适应面临两项重大挑战。i) 噪声动态性。现有结构学习方法难以应对噪声的时间方面特征，限制了其在动态且不断变化的噪声模式中的有效性。ii) 更严重的噪声。两个节点之间的多次交互可能引入噪声，导致这些节点被重新污染，从而相比静态图引发更严重的噪声问题。本文提出RDGSL——一种连续时间动态图的表示学习方法。同时，我们提出了动态图结构学习这一新型监督信号，使RDGSL具备有效对抗动态图中噪声的能力。为解决噪声动态性问题，我们引入了动态图过滤器，并创新性地提出动态噪声函数，该函数能动态捕捉当前及历史噪声，从而评估噪声的时间方面特征并生成去噪图。我们进一步提出时序嵌入学习器以应对更严重噪声的挑战，该学习器利用注意力机制选择性忽略噪声边，专注于正常边，增强表示生成对噪声的鲁棒性。我们的方法在下游任务中展现出鲁棒性，在演化分类任务中相较于第二名基线方法实现了高达5.1%的绝对AUC提升。