Detecting hate speech in online communities is increasingly challenging due to the implicit and context-dependent nature of toxic expressions. While text-only models often struggle with such ambiguity, incorporating user interaction signals offers critical pragmatic context for disambiguation. However, research in this direction is hindered by the scarcity of datasets that align textual content with comprehensive user behavioral graphs. To bridge this gap, we present a new dataset collected from a real-world community, featuring labeled hate speech enriched with fine-grained interaction histories. We further propose a novel user-aware hate speech detection framework that effectively fuses textual semantics with social interaction representations. Experiments demonstrate that our approach consistently outperforms strong text-only baselines by over 3.6%, validating the critical role of social context in enhancing detection accuracy. Furthermore, to mitigate real-world adversarial risks such as graph spoofing and spam, we introduce a contrastive graph augmentation strategy, ensuring model robustness against unreliable community behaviors.