Leaf Powers and Their Properties: Using the Trees

  • Authors:
  • Michael R. Fellows;Daniel Meister;Frances A. Rosamond;R. Sritharan;Jan Arne Telle

  • Affiliations:
  • University of Newcastle, Newcastle, Australia;Department of Informatics, University of Bergen, Norway;University of Newcastle, Newcastle, Australia;Department of Computer Science, The University of Dayton, Dayton, USA;Department of Informatics, University of Bergen, Norway

  • Venue:
  • ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
  • Year:
  • 2008

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Abstract

A graph G on n vertices is a k-leaf power ($G \in {\cal G}_{k}$) if it is isomorphic to a graph that can be "generated" from a tree T that has n leaves, by taking the leaves to represent vertices of G, and making two vertices adjacent if and only if they are at distance at most k in T. We address two questions in this paper:(1) As k increases, do we always have ${\cal G}_{k} \subseteq {\cal G}_{k+1}$ ? Answering an open question of Andreas Brandstädt and Van Bang Le [2,3,1], we show that the answer, perhaps surprisingly, is "no."(2) How should one design algorithms to determine, for k-leaf powers, if they have some property?One way this can be done is to use the fact that k-leaf powers have bounded cliquewidth. This fact, plus the FPT cliquewidth approximation algorithm of Oum and Seymour [14], combined with the results of Courcelle, Makowsky and Rotics [7], allows us to conclude that properties expressible in a general logic formalism, can be decided in FPT time for k-leaf powers, parameterizing by k. This is wildly inefficient. We explore a different approach, under the assumption that a generating tree is given with the graph. We show that one can use the tree directly to decide the property, by means of a finite-state tree automaton. (A more general theorem has been independently obtained by Blumensath and Courcelle [5].)We place our results in a general context of "tree-definable" graph classes, of which k-leaf powers are one particular example.