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Dedicated hardware for spiking neural networks (SNN) reduces energy consumption with spike-driven computing. This paper proposes a graph-based spatio-temporal backpropagation (G-STBP) to train SNN, aiming to enhance spike sparsity for energy efficiency, while ensuring the accuracy. A differentiable leaky integrate-and-fire (LIF) model is suggested to establish the backpropagation path. The sparse regularization is proposed to reduce the spike firing rate with a guaranteed accuracy. GSTBP enables training in any network topologies thanks to graph representation. A recurrent network is demonstrated with spike-sparse rank order coding. The experimental result on rank order coded MNIST shows that the recurrent SNN trained by G-STBP achieves the accuracy of 97.3% using 392 spikes per inference.