Title: Relational Curriculum Learning for Graph Neural Networks. Abstract: Graph neural networks have achieved great success in representing structured data and its downstream tasks such as node classification. The key idea is to recursively propagate and aggregate information along the edges of a given graph topology. 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 graph neural network models may lead to suboptimal learned representations because they usually consider every edge in a given graph topology equally. On the other hand, curriculum learning, 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 of representation learners by gradually proceeding from easy to more difficult samples during training. Unfortunately, most existing curriculum learning strategies are designed for i.i.d data samples and cannot be trivially generalized to handle structured data with dependencies. In order to address these issues, in this paper we propose a novel curriculum learning method for structured data to leverage the various underlying difficulties of data dependencies to improve the quality of learned representations on structured data. Specifically, we design a learning strategy that gradually incorporates edges in a given graph topology into training according to their difficulty from easy to hard, where the degree of difficulty is measured by a self-supervised learning paradigm. We demonstrate the strength of our proposed method in improving the generalization ability of learned representations through extensive experiments on nine synthetic datasets and seven real-world datasets with different commonly used graph neural network models as backbone models.