Blind Community Detection from Low-Rank Excitations of a Graph Filter

Hoi To Wai, Santiago Segarra, Asuman E. Ozdaglar, Anna Scaglione, Ali Jadbabaie

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number8937488
Pages (from-to)436-451
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume68
DOIs
StatePublished - Jan 1 2020

    Fingerprint

Keywords

  • Community detection
  • graph signal processing
  • low-rank matrix recovery
  • spectral clustering

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this