Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing.

We propose a computationally efficient procedure for elevated mean detection on a connected subgraph of a network with node-related scalar observations.Our approach relies on two intuitions: first, a significant concentration of high observations in a connected subgraph implies that the subgraph induced by the nodes associated with the highest observations has a large connected component.Secondly, a greater detection power can be obtained in certain cases by denoising the observations using the network structure.Numerical experiments show that our procedure's detection performance and computational efficiency are both competitive.