Blind Community Detection From Low-Rank Excitations of a Graph Filter
Name
1809.01485.pdf
Description
Submitted version
Size
1.2 MB
Format
Adobe PDF
Checksum (MD5)
18685f704d77744d1ff3bc56dd290c2e
Author(s)
Wai, Hoi-To
Segarra, Santiago M
Ozdaglar, Asuman E.
Scaglione, Anna
Jadbabaie-Moghadam, Ali
Date Issued
December 2019
Journal
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wai, Hoi-To et al. "Blind Community Detection From Low-Rank Excitations of a Graph Filter." IEEE Transactions on Signal Processing 68 (December 2019): 436 - 451 © 2020 IEEE
Version
Original manuscript
Abstract
© 1991-2012 IEEE. This paper considers a new framework to detect communities in a graph from the observation of signals at its nodes. We model the observed signals as noisy outputs of an unknown network process, represented as a graph filter that is excited by a set of unknown low-rank inputs/excitations. Application scenarios of this model include diffusion dynamics, pricing experiments, and opinion dynamics. Rather than learning the precise parameters of the graph itself, we aim at retrieving the community structure directly. The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals. Our analysis indicates that the community detection performance depends on an intrinsic 'low-pass' property of the graph filter. We also show that the performance can be improved via a low-rank matrix plus sparse decomposition method when the latent parameter vectors are known. Numerical results demonstrate that our approach is effective.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tsp.2019.2961296
