Applying Meta-learning

We introduce a meta-learning technique to learn more adaptive graph prompts for multi-task settings. Meta-learning is applied over multiple tasks to learn better prompts. This process involves constructing meta prompting tasks and updating the parameters of the tasks using a gradient descent method.

To improve the learning stability, we organize these tasks as multi-task episodes where each episode contains batch tasks including node classification (“𝑛” for short), edge classification (“ℓ” for short), and graph classification (“𝑔” for short).

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