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Graph Coloring via Degeneracy in Streaming and Other Space-Conscious Models

Authors Suman K. Bera, Amit Chakrabarti , Prantar Ghosh

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Author Details

Suman K. Bera
  • University of California at Santa Cruz, CA, USA
Amit Chakrabarti
  • Dartmouth College, Hanover, NH, USA
Prantar Ghosh
  • Dartmouth College, Hanover, NH, USA

Funding

This work was supported in part by NSF under award CCF-190773 and by the Simons Institute for the Theory of Computing, which hosted the second author while the work was in progress.

Acknowledgements

The authors gratefully acknowledge several helpful discussions with Sepehr Assadi (especially those that called to their attention a nuance with the Congested Clique algorithm) and Deeparnab Chakrabarty.

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Suman K. Bera, Amit Chakrabarti, and Prantar Ghosh. Graph Coloring via Degeneracy in Streaming and Other Space-Conscious Models. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 11:1-11:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.ICALP.2020.11

Abstract

We study the problem of coloring a given graph using a small number of colors in several well-established models of computation for big data. These include the data streaming model, the general graph query model, the massively parallel communication (MPC) model, and the CONGESTED-CLIQUE and the LOCAL models of distributed computation. On the one hand, we give algorithms with sublinear complexity, for the appropriate notion of complexity in each of these models. Our algorithms color a graph G using κ(G)⋅(1+o(1)) colors, where κ(G) is the degeneracy of G: this parameter is closely related to the arboricity α(G). As a function of κ(G) alone, our results are close to best possible, since the optimal number of colors is κ(G)+1. For several classes of graphs, including real-world "big graphs," our results improve upon the number of colors used by the various (Δ(G)+1)-coloring algorithms known for these models, where Δ(G) is the maximum degree in G, since Δ(G) ⩾ κ(G) and can in fact be arbitrarily larger than κ(G). On the other hand, we establish certain lower bounds indicating that sublinear algorithms probably cannot go much further. In particular, we prove that any randomized coloring algorithm that uses at most κ(G)+O(1) colors would require Ω(n²) storage in the one pass streaming model, and Ω(n²) many queries in the general graph query model, where n is the number of vertices in the graph. These lower bounds hold even when the value of κ(G) is known in advance; at the same time, our upper bounds do not require κ(G) to be given in advance.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming models
  • Theory of computation → Dynamic graph algorithms
  • Theory of computation → Sketching and sampling
  • Theory of computation → Lower bounds and information complexity
Keywords
  • Data streaming
  • Graph coloring
  • Sublinear algorithms
  • Massively parallel communication
  • Distributed algorithms

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