Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. However, existing graph-based methods often focus on static graph structures, which struggle to adapt to the evolving nature of these graphs. In this paper, we propose Adaptive-DecayRank, a real-time and adaptive anomaly detection model for dynamic graph streams. Our method extends the dynamic PageRank algorithm by incorporating an adaptive Bayesian updating mechanism, allowing nodes to dynamically adjust their decay factors based on observed graph changes. This enables real-time detection of sudden structural shifts, improving anomaly identification in streaming graphs. We evaluate Adaptive-DecayRank on multiple real-world security datasets, including DARPA and CTU-13, as well as synthetic dense graphs generated using RTM. Our experiments demonstrate that Adaptive-DecayRank outperforms state-of-the-art methods, such as AnomRank, Sedanspot, and DynAnom, achieving up to $13.94\%$ higher precision, $8.43\%$ higher AUC, and more robust detection in highly dynamic environments.

People living with HIV (PLWH) faced diverse challenges during the COVID-19 pandemic, including disruptions to care, housing instability, emotional distress, and economic hardship. This study used graph-based clustering methods to analyze pandemic-era experiences of PLWH in a national sample from the NIH’s All of Us dataset (n = 242). Across three graph configurations we identified consistent subgroups shaped by social connectedness, housing stability, emotional well-being, and engagement with preventive behaviors. Comparison with an earlier local study of PLWH in Illinois confirmed recurring patterns of vulnerability and resilience while also revealing additional national-level subgroups not observed in the smaller sample. Subgroups with strong social or institutional ties were associated with greater emotional stability and proactive engagement with COVID-19 preventive behaviors, while those facing isolation and structural hardship exhibited elevated distress and limited engagement with COVID-19 preventive measures. These findings underscore the importance of precision public health strategies that reflect the heterogeneity of PLWH and suggest that strengthening social support networks, promoting housing stability, and leveraging institutional connections may enhance pandemic preparedness and HIV care in future public health crises.