Social media platforms have become critical sources of patient-generated health data, yet existing computational approaches fail to capture the interconnected nature of online health discourse. We present a novel framework that integrates graph-based community detection with large language model analysis to understand patient narratives in multimodal social media content. Applied to 10,253 TikTok posts about JAK inhibitors (2020-2024), our approach constructs heterogeneous graphs representing user-content-medical entity relationships and applies community detection algorithms enhanced with context-aware LLM interpretation. Our comprehensive analysis of 10,253 posts (January 2020–September 2024) reveals five distinct patient communities characterized by different discourse patterns: treatment success narratives (873 nodes), medication guidance (642 nodes), side effect discussions (589 nodes), comparative treatment analysis (412 nodes), and dosage optimization (347 nodes). The Louvain algorithm significantly outperformed Girvan-Newman in modularity (0.9931 vs. 0.9928), conductance (0.0002 vs. 0.0006), and computational efficiency (0.14s vs. 54.24s). Temporal analysis demonstrates increasing community cohesion and evolving discourse patterns from cautious inquiry (2020-2021) to experience sharing and specialized sub-communities (2023-2024). This work contributes: (1) a scalable computational framework for multimodal health content analysis, (2) methodological innovations in graph-LLM integration, and (3) insights into platform-specific health communication patterns. The framework has applications in pharmacovigilance, computational social science, and AI-assisted health monitoring systems.