  Temporal networks are effective in capturing the evolving interactions of
networks over time, such as social networks and e-commerce networks. In recent
years, researchers have primarily concentrated on developing specific model
architectures for Temporal Graph Neural Networks (TGNNs) in order to improve
the representation quality of temporal nodes and edges. However, limited
attention has been given to the quality of negative samples during the training
of TGNNs. When compared with static networks, temporal networks present two
specific challenges for negative sampling: positive sparsity and positive
shift. Positive sparsity refers to the presence of a single positive sample
amidst numerous negative samples at each timestamp, while positive shift
relates to the variations in positive samples across different timestamps. To
robustly address these challenges in training TGNNs, we introduce Curriculum
Negative Mining (CurNM), a model-aware curriculum learning framework that
adaptively adjusts the difficulty of negative samples. Within this framework,
we first establish a dynamically updated negative pool that balances random,
historical, and hard negatives to address the challenges posed by positive
sparsity. Secondly, we implement a temporal-aware negative selection module
that focuses on learning from the disentangled factors of recently active
edges, thus accurately capturing shifting preferences. Extensive experiments on
12 datasets and 3 TGNNs demonstrate that our method outperforms baseline
methods by a significant margin. Additionally, thorough ablation studies and
parameter sensitivity experiments verify the usefulness and robustness of our
approach. Our code is available at https://github.com/zziyue83/CurNM.
