Community Inference from Graph Signals with Hidden Nodes

Hoi To Wai, Yonina C. Eldar, Asuman E. Ozdaglar, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many recent works on inference of graph structure assume that the graph signals are fully observable. For large graphs with thousands or millions of nodes, this entails high complexity on the data collection and processing steps. Here, we study a community inference problem on partially observed (sub-sampled) graph signals which sidesteps topology inference, while revealing the coarse structure of the graph directly. Two variants of the inference task are studied: (i) a blind method that infers the communities that the observable nodes belong to; and (ii) a semi-blind method that infers the communities of all nodes using, in addition, side information about the sub-graph between observable and hidden nodes. These techniques for community inference are shown to be efficient and suitable for large graphs analytically and empirically.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4948-4952
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 1 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period5/12/195/17/19

Keywords

  • community inference
  • graph signal processing
  • hidden nodes
  • topology inference

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Community Inference from Graph Signals with Hidden Nodes'. Together they form a unique fingerprint.

  • Cite this

    Wai, H. T., Eldar, Y. C., Ozdaglar, A. E., & Scaglione, A. (2019). Community Inference from Graph Signals with Hidden Nodes. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 4948-4952). [8683001] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683001