  Temporal Graph Learning, which aims to model the time-evolving nature of
graphs, has gained increasing attention and achieved remarkable performance
recently. However, in reality, graph structures are often incomplete and noisy,
which hinders temporal graph networks (TGNs) from learning informative
representations. Graph contrastive learning uses data augmentation to generate
plausible variations of existing data and learn robust representations.
However, rule-based augmentation approaches may be suboptimal as they lack
learnability and fail to leverage rich information from downstream tasks. To
address these issues, we propose a Time-aware Graph Structure Learning (TGSL)
approach via sequence prediction on temporal graphs, which learns better graph
structures for downstream tasks through adding potential temporal edges. In
particular, it predicts time-aware context embedding based on previously
observed interactions and uses the Gumble-Top-K to select the closest candidate
edges to this context embedding. Additionally, several candidate sampling
strategies are proposed to ensure both efficiency and diversity. Furthermore,
we jointly learn the graph structure and TGNs in an end-to-end manner and
perform inference on the refined graph. Extensive experiments on temporal link
prediction benchmarks demonstrate that TGSL yields significant gains for the
popular TGNs such as TGAT and GraphMixer, and it outperforms other contrastive
learning methods on temporal graphs. We release the code at
https://github.com/ViktorAxelsen/TGSL.
