Network Inference from Graph Dynamic Processes

We address the problem of identifying the structure of an undirected graph from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. Our approach leverages concepts from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end, we develop efficient inference algorithms stemming from provably tight convex relaxations of natural non-convex criteria. For scenarios where the number of samples is insufficient for exact graph recovery, we show that coarser graph features (such as communities) can still be correctly inferred.


April 3 @ 12:30
12:30 pm (1h)

Discovery Building, Orchard View Room

Santiago Segarra