Random Walk Sampling for Big Data over Networks
This work addresses sampling challenges for big data over networks, but it appears incremental as it builds on existing recovery conditions.
The paper tackles the problem of accurately recovering smooth graph signals from few samples by proposing a random walk sampling strategy based on the network nullspace property, demonstrating effectiveness in synthetic and real-world datasets.
It has been shown recently that graph signals with small total variation can be accurately recovered from only few samples if the sampling set satisfies a certain condition, referred to as the network nullspace property. Based on this recovery condition, we propose a sampling strategy for smooth graph signals based on random walks. Numerical experiments demonstrate the effectiveness of this approach for graph signals obtained from a synthetic random graph model as well as a real-world dataset.