The integration of sequential and social recommendations using a graph-based co-attention architecture marks a significant advancement in deep learning, substantially improving recommender system performance. This innovation enables effective representation learning of user interactions by capturing dynamic preferences influenced by both sequential and social contexts. While prior research has attempted to model evolving user preferences, existing approaches suffer from two key limitations: 1) ineffective modeling of complex sequential dependencies and 2) overlooking the hierarchical nature of social influence, resulting in suboptimal performance. To address these challenges, we propose GCA4Rec (Graph-Based Co-Attention Networks for Sequential and Social Recommendation), a novel framework designed to handle the complexities of user preference dynamics across sequential and social domains. Our model combines a graph attention contrastive learning module to track sequential preference shifts, while a hyperbolic graph attention isomorphism network models social-level preference dynamics. Additionally, we introduce fusionGated, a novel gating mechanism that effectively integrates co-attention signals from both levels. Extensive experiments on real-world benchmark datasets demonstrate that GCA4Rec outperforms state-of-the-art methods in Top-k recommendation tasks and exhibits robustness under varying degrees of data sparsity.