Storage capacity of labeled graphs

  • Authors:
  • Dana Angluin;James Aspnes;Rida A. Bazzi;Jiang Chen;David Eisenstat;Goran Konjevod

  • Affiliations:
  • Department of Computer Science, Yale University;Department of Computer Science, Yale University;Department of Computer Science and Engineering, Arizona State University;Google;Department of Computer Science, Brown University;Department of Computer Science and Engineering, Arizona State University

  • Venue:
  • SSS'10 Proceedings of the 12th international conference on Stabilization, safety, and security of distributed systems
  • Year:
  • 2010

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Abstract

We consider the question of how much information can be stored by labeling the vertices of a connected undirected graph G using a constant-size set of labels, when isomorphic labelings are not distinguishable. An exact information-theoretic bound is easily obtained by counting the number of isomorphism classes of labelings of G, which we call the information-theoretic capacity of the graph. More interesting is the effective capacity of members of some class of graphs, the number of states distinguishable by a Turing machine that uses the labeled graph itself in place of the usual linear tape. We show that the effective capacity equals the information-theoretic capacity up to constant factors for trees, random graphs with polynomial edge probabilities, and bounded-degree graphs.