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Session-based recommendations have attracted significant attention because of their broad application scenarios. Recently, graph neural networks (GNNs) have been employed in session-based recommendation because of their superior performance in representation learning compared with recurrent neural networks (RNNs). Although most existing GNN-based methods have made great achievements in this field, none of them emphasizes the importance of repeat recommendations, which has been an important component in session-based recommendation (e.g., people tend to browse product information repeatedly or revisit websites in a period of time). In this paper, we propose a novel model called ReGNN to combine a graph neural network with a repeat-exploration mechanism for better recommendations. Specifically, we dynamically process the item sequence of a session as a graph structure and capture the complex transitions between items by a GNN. Then, we formulate an exact session representation with the attention mechanism. Finally, the repeat-exploration mechanism is incorporated into the ReGNN to model the user’s repeat-exploration behavior patterns and make more accurate predictions. We conduct extensive experiments on two public datasets. The experimental results show that our proposed model ReGNN consistently outperforms other state-of-the-art methods.