Existing streaming graph anomaly detection methods only detect edge or subgraph anomalies. We extend count-min sketch to higher-order preserving the dense subgraph structure & detect both. Our approach is the first streaming method that uses dense subgraph search to detect graph anomalies in constant memory and time. Preprint of the paper is at
https://arxiv.org/pdf/2106.04486.pdf