Estimating Centrality Blindly from Low-pass Filtered Graph Signals
arXiv:1910.13137
Abstract
This paper considers blind methods for centrality estimation from graph signals. We model graph signals as the outcome of an unknown low-pass graph filter excited with influences governed by a sparse sub-graph. This model is compatible with a number of data generation process on graphs, including stock data and opinion dynamics. Based on the said graph signal model, we first prove that the folklore heuristics based on PCA of data covariance matrix may fail when the graph filter is not sufficiently low-pass. To remedy, we propose a robust blind centrality estimation method which substantially improves the centrality estimation performance. Numerical results on synthetic and real data support our findings.
arXiv comment5 pages, 1 figure, submitted to ICASSP2020
References in corpus (9)
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Discrete Signal Processing on Graphs
- Learning graphs from data: A signal representation perspective
- Connecting the Dots: Identifying Network Structure via Graph Signal Processing
- Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies
- Revealing Network Connectivity From Dynamics
- Sampling of graph signals with successive local aggregations
- Active Sensing of Social Networks
- Blind Community Detection from Low-rank Excitations of a Graph Filter