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Symmetric Sparse Boolean Matrix Factorization and Applications

Authors Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang

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Author Details

Sitan Chen
  • University of California, Berkeley, CA, USA
Zhao Song
  • Adobe Research, Seattle, WA, USA
Runzhou Tao
  • Columbia University, New York, NY, USA
Ruizhe Zhang
  • University of Texas at Austin, TX, USA

Funding

  • Chen, Sitan: This work was supported in part by NSF Award 2103300, NSF CAREER Award CCF-1453261, NSF Large CCF-1565235 and Ankur Moitra’s ONR Young Investigator Award. Part of this work was completed while visiting the Simons Institute for the Theory of Computing.
  • Zhang, Ruizhe: This work was supported by Dana Moshkovitz’s NSF Grant CCF-1648712.

Cite As Get BibTex

Sitan Chen, Zhao Song, Runzhou Tao, and Ruizhe Zhang. Symmetric Sparse Boolean Matrix Factorization and Applications. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 46:1-46:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ITCS.2022.46

Abstract

In this work, we study a variant of nonnegative matrix factorization where we wish to find a symmetric factorization of a given input matrix into a sparse, Boolean matrix. Formally speaking, given {𝐌} ∈ {ℤ}^{m× m}, we want to find {𝐖} ∈ {0,1}^{m× r} such that ‖ {𝐌} - {𝐖} {𝐖}^⊤ ‖₀ is minimized among all {𝐖} for which each row is k-sparse. This question turns out to be closely related to a number of questions like recovering a hypergraph from its line graph, as well as reconstruction attacks for private neural network training. 
As this problem is hard in the worst-case, we study a natural average-case variant that arises in the context of these reconstruction attacks: {𝐌} = {𝐖} {𝐖}^{⊤} for {𝐖} a random Boolean matrix with k-sparse rows, and the goal is to recover {𝐖} up to column permutation. Equivalently, this can be thought of as recovering a uniformly random k-uniform hypergraph from its line graph.
Our main result is a polynomial-time algorithm for this problem based on bootstrapping higher-order information about {𝐖} and then decomposing an appropriate tensor. The key ingredient in our analysis, which may be of independent interest, is to show that such a matrix {𝐖} has full column rank with high probability as soon as m = Ω̃(r), which we do using tools from Littlewood-Offord theory and estimates for binary Krawtchouk polynomials.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Matrix factorization
  • tensors
  • random matrices
  • average-case complexity

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