Social recommendation enhances the learning of user preferences by incorporating user social information. Recently, graph neural network models have gradually become the subject of the social recommendation. However, most graph neural network-based approaches fail to fully learn the high-order collaborative semantics of user interest and social domains, and ignore the unique self-supervised signals in user social domains. To alleviate these problems, we propose a novel lightweight GCN-based social recommendation method SGSR that jointly models the high-order collaborative relations of user/item nodes in both domains. Meanwhile, in the process of message transmission of the bipartite graph and social graph, we respectively introduce a self-attention mechanism to measure the contributions of different nodes. In particular, to take full advantage of the self-supervised signals between user node messages in the social domain, we innovatively incorporate contrastive learning into this system to enable user-side node features to self-learn and update. Extensive experiments conducted on two real datasets demonstrate the effectiveness and necessity of our proposed approach.