Anomaly detection in dynamic systems is a critical area of research, particularly due to the increasing complexity and interconnectivity of modern networks. This paper reviews distinct graph-based techniques for detecting anomalies, focusing on their applicability in dynamic environments. We discuss the theoretical foundations of graph theory, various graph neural network (GNN) architectures, embedding techniques, and the integration of federated learning. Challenges unique to dynamic systems are also highlighted, along with future research directions.