Hyper-Relational Anomaly Detection in Streaming Graph Databases via Contrastive Hypervector Embedding

**Abstract:** The increasing prevalence of streaming graph databases demands robust anomaly detection strategies that can dynamically adapt to evolving relationships and patterns. This paper introduces a novel approach, Contrastive Hypervector Embedding for Anomaly Detection in Streaming Graphs (CHED-SG), which leverages hyperdimensional computing (HDC) and contrastive learning to identify anomalous nodes and edges in real-time. CHED-SG efficiently transforms graph structures into hypervector embeddings, enabling rapid anomaly scoring and adaptive thresholding. Our method exhibits significant advantages in scalability and accuracy compared to traditional graph anomaly detection techniques, particularly in highly dynamic environments. The hybrid framework is immediately commercializable and optimized for implementation by researchers and engineers.