2019/09/06 by Junhao Yu, Yu, Junhao, Xuan Xie +5 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Age of Information Optimization #Complex Network Analysis Techniques #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1909.02692
openalex publication_date 2019/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Joint time-vertex graph signals are pervasive in real-world. This paper focuses on the fundamental problem of sampling and reconstruction of joint time-vertex graph signals. We prove the existence and the necessary condition of a critical sampling set using minimum number of samples in time and graph domain respectively. The theory proposed in this paper suggests to assign heterogeneous sampling pattern for each node in a network under the constraint of minimum resources. An efficient algorithm is also provided to construct a critical sampling set.