Tree Path Majority Data Structures

Authors Travis Gagie, Meng He, Gonzalo Navarro

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Travis Gagie
  • CeBiB - Center for Biotechnology and Bioengineering, Chile, School of Computer Science and Telecommunications, Diego Portales University, Chile
Meng He
  • Faculty of Computer Science, Dalhousie University, Canada
Gonzalo Navarro
  • CeBiB - Center for Biotechnology and Bioengineering, Chile, IMFD - Millenium Institute for Foundational Research on Data, Chile, Dept. of Computer Science, University of Chile, Chile

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Travis Gagie, Meng He, and Gonzalo Navarro. Tree Path Majority Data Structures. In 29th International Symposium on Algorithms and Computation (ISAAC 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 123, pp. 68:1-68:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


We present the first solution to tau-majorities on tree paths. Given a tree of n nodes, each with a label from [1..sigma], and a fixed threshold 0<tau<1, such a query gives two nodes u and v and asks for all the labels that appear more than tau * |P_{uv}| times in the path P_{uv} from u to v, where |P_{uv}| denotes the number of nodes in P_{uv}. Note that the answer to any query is of size up to 1/tau. On a w-bit RAM, we obtain a linear-space data structure with O((1/tau)lg^* n lg lg_w sigma) query time. For any kappa > 1, we can also build a structure that uses O(n lg^{[kappa]} n) space, where lg^{[kappa]} n denotes the function that applies logarithm kappa times to n, and answers queries in time O((1/tau)lg lg_w sigma). The construction time of both structures is O(n lg n). We also describe two succinct-space solutions with the same query time of the linear-space structure. One uses 2nH + 4n + o(n)(H+1) bits, where H <=lg sigma is the entropy of the label distribution, and can be built in O(n lg n) time. The other uses nH + O(n) + o(nH) bits and is built in O(n lg n) time w.h.p.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
  • Majorities on Trees
  • Succinct data structures


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