  Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the
edges. However, edges in real-world graphs often have varying degrees of
difficulty, and some edges may even be noisy to the downstream tasks.
Therefore, existing GNNs may lead to suboptimal learned representations because
they usually treat every edge in the graph equally. On the other hand,
Curriculum Learning (CL), which mimics the human learning principle of learning
data samples in a meaningful order, has been shown to be effective in improving
the generalization ability and robustness of representation learners by
gradually proceeding from easy to more difficult samples during training.
Unfortunately, existing CL strategies are designed for independent data samples
and cannot trivially generalize to handle data dependencies. To address these
issues, we propose a novel CL strategy to gradually incorporate more edges into
training according to their difficulty from easy to hard, where the degree of
difficulty is measured by how well the edges are expected given the model
training status. We demonstrate the strength of our proposed method in
improving the generalization ability and robustness of learned representations
through extensive experiments on nine synthetic datasets and nine real-world
datasets. The code for our proposed method is available at
https://github.com/rollingstonezz/Curriculum_learning_for_GNNs.
