Blind Community Detection From Low-Rank Excitations of a Graph Filter
Author(s)Wai, Hoi-To; Segarra, Santiago M; Ozdaglar, Asuman E.; Scaglione, Anna; Jadbabaie-Moghadam, Ali
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© 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.
DepartmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems; MIT Institute for Data, Systems, and Society; Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
IEEE Transactions on Signal Processing
Institute of Electrical and Electronics Engineers (IEEE)
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