Unlike whole-body action recognition, hand gestures involve spatially closely distributed joints, promoting stronger collaboration. This needs to be taken into account in order to capture complex spatial and temporal features. In response to these challenges, this paper presents a Spatio-Temporal Sparse Graph Convolution Network (ST-SGCN) for dynamic recognition of hand gestures. Based on decoupled spatio-temporal processing, the ST-SGCN incorporates Graph Convolutional Networks, attention mechanism and asymmetric convolutions to capture the nuanced movements of hand joints. The key novelty is the introduction of sparse spatio-temporal directed interactions, overcoming the limitations associated with dense, undirected methods. The sparse aspect models essential interactions between hand joints selectively, improving computational efficiency and interpretability. Directed interactions capture asymmetrical dependencies between hand joints, improving discernment of joint influences. Experimental evaluations on three benchmark datasets, including Briareo, SHREC'17 and IPN Hand, demonstrate ST-SGCN's state-of-the-art performance for dynamic hand gesture recognition. Codes are available at: https://github.com/HichemSaoudi/ST-SGCN