  Supervised learning, while prevalent for information cascade modeling, often
requires abundant labeled data in training, and the trained model is not easy
to generalize across tasks and datasets. It often learns task-specific
representations, which can easily result in overfitting for downstream tasks.
Recently, self-supervised learning is designed to alleviate these two
fundamental issues in linguistic and visual tasks. However, its direct
applicability for information cascade modeling, especially graph cascade
related tasks, remains underexplored. In this work, we present Contrastive
Cascade Graph Learning (CCGL), a novel framework for information cascade graph
learning in a contrastive, self-supervised, and task-agnostic way. In
particular, CCGL first designs an effective data augmentation strategy to
capture variation and uncertainty by simulating the information diffusion in
graphs. Second, it learns a generic model for graph cascade tasks via
self-supervised contrastive pre-training using both unlabeled and labeled data.
Third, CCGL learns a task-specific cascade model via fine-tuning using labeled
data. Finally, to make the model transferable across datasets and cascade
applications, CCGL further enhances the model via distillation using a
teacher-student architecture. We demonstrate that CCGL significantly
outperforms its supervised and semi-supervised counterparts for several
downstream tasks.
