Experience Sampling Method (ESM) has emerged as a technique for capturing real-time mental health data in natural environments, offering advantages over traditional retrospective assessments by reducing recall bias and providing contextual understanding of emotional patterns. Despite its benefits, ESM remains underutilized due to limited tools for transforming complex datasets into interpretable insights for clinicians and patients. This study developed and evaluated a network graph visualization to represent behavior-emotion relationships from ESM data. Six mental health practitioners evaluated the system through structured surveys assessing usability, clinical relevance, and interpretive capability. Participants rated visualization intuitiveness at 3.8/7 and visual design at 3.2/5. Comparative evaluations were mixed, with participants rating the approach as better (n=2), equivalent (n=2), worse (n=1), or much worse (n=1) than traditional methods. Despite usability challenges related to visual complexity and dynamic node movement, participants successfully extracted clinically relevant behavior-emotion patterns. Color coding was the most effective design element, while interactive filtering functionality was crucial for pattern recognition. Network visualization shows potential for making ESM data more accessible to mental health practitioners, though design refinements addressing visual complexity and temporal dynamics integration are needed to improve clinical utility.