The National Science Foundation awarded Rowan University a $273,047 Project Grant under the Engineering (47.041) federal grant program. The three-year award will fund research to develop an integrated real-time anomaly detection and explanation framework for complex dynamic systems modeled as graphs. Specifically, the university researchers will design computational strategies to reduce false positives and detection delays in dynamic graph anomaly detection. They will also explore a new time-dependent explanation model to provide context around anomaly identifications in graphs. The framework aims to enable early warnings, predictions, and trustworthy explanations of anomalies to support decision-making. If successful, the research could benefit agencies and organizations requiring timely resource allocation and mitigation planning for abnormal events.