RDGSL: Dynamic Graph Representation Learning with Structure Learning
Siwei Zhang Yun Xiong Yao Zhang Yiheng Sun Xi Chen Yizhu Jiao Yangyong Zhu
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.