Compressing data on graphs with clusters

Amir R. Asadi, Emmanuel Abbe, Sergio Verdú

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

8 Scopus citations

Abstract

This paper investigates the fundamental limits for compressing data on graphs, exploiting dependencies due to community structures in the graph. The source model, referred to as the data block model (DBM), is a mixture of discrete memoryless sources determined by the community structure of a stochastic block model (SBM). The main result gives the optimal expected length of a lossless compressor when the community signal is strong enough, a condition on the edge probabilities and the data distributions, which can take place below the exact recovery threshold of the SBM. This is derived in part by obtaining the threshold for exact recovery in SBMs with strong side information, a result of independent interest, which extends the CH-divergence threshold. Finally we discuss compressing data with almost exact recovery algorithms.

Original languageEnglish (US)
Title of host publication2017 IEEE International Symposium on Information Theory, ISIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1583-1587
Number of pages5
ISBN (Electronic)9781509040964
DOIs
StatePublished - Aug 9 2017
Event2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany
Duration: Jun 25 2017Jun 30 2017

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other2017 IEEE International Symposium on Information Theory, ISIT 2017
CountryGermany
CityAachen
Period6/25/176/30/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Asadi, A. R., Abbe, E., & Verdú, S. (2017). Compressing data on graphs with clusters. In 2017 IEEE International Symposium on Information Theory, ISIT 2017 (pp. 1583-1587). [8006796] (IEEE International Symposium on Information Theory - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2017.8006796