Detecting Activations over Graphs using Spanning Tree Wavelet Bases
We consider the detection of clusters of activation over graphs under Gaussian noise. This problem appears in many real world scenarios, such as the detecting contamination or seismic activity by sensor networks, viruses in human and computer networks, and groups with anomalous behavior in social and biological networks. Despite the wide applicability of such a detection algorithm, there has been little success in the development of computationally feasible methods with provable theoretical guarantees. To this end, we introduce the spanning tree wavelet basis over a graph, a localized basis that reflects the topology of the graph. We first provide a necessary condition for asymptotic distinguishability of the null and alternative hypotheses. Then we prove that for any spanning tree, we can hope to correctly detect signals in a low signal-to-noise regime using spanning tree wavelets. We propose a randomized test, in which we use a uniform spanning tree in the basis construction. Using electrical network theory, we show that the uniform spanning tree provides strong guarantees that in many cases match our necessary condition. We prove that for edge transitive graphs, k-nearest neighbor graphs, and ϵ-graphs we obtain nearly optimal performance with the uniform spanning tree wavelet detector.