The aim of this project is to propose a least mean squares (LMS) strategy for adaptive estimation of sigals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean square error, and tracking from a limited number of observations over a subset of vertices.
Furthermore, to cope with the case where the bandwidth is not known beforehand, we propose a method that performs a sparse online estimation of the signal support in the (graph) frequency domain, which enables online adaptation of the graph sampling strategy.