Practical graph signal sampling with log-linear size scaling

Graph signal sampling is the problem of selecting a subset representative graph vertices whose values can be used to interpolate missing on remaining vertices. Optimizing choice set using concepts from experiment design help minimize effect noise in input signal. While many existing selection methods are computationally intensive because they require an eigendecomposition, eigendecomposition-free still much slower than random algorithms for large graphs. In this paper, through optimizing sets towards D-optimal objective design, we propose algorithm that has complexity comparable algorithms, while reaching accuracy similar broad range types.