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Balanced families of perfect hash functions and their applications

Authors: Noga Alon; Shai Gutner;

Balanced families of perfect hash functions and their applications

Abstract

The construction of perfect hash functions is a well-studied topic. In this article, this concept is generalized with the following definition. We say that a family of functions from [ n ] to [ k ] is a δ-balanced ( n,k )-family of perfect hash functions if for every S ⊆ [ n ], | S |= k , the number of functions that are 1-1 on S is between T /δ and δ T for some constant T >0. The standard definition of a family of perfect hash functions requires that there will be at least one function that is 1-1 on S , for each S of size k . In the new notion of balanced families, we require the number of 1-1 functions to be almost the same (taking δ to be close to 1) for every such S . Our main result is that for any constant δ > 1, a δ-balanced ( n,k )-family of perfect hash functions of size 2 O ( k log log k ) log n can be constructed in time 2 O ( k log log k ) n log n . Using the technique of color-coding we can apply our explicit constructions to devise approximation algorithms for various counting problems in graphs. In particular, we exhibit a deterministic polynomial-time algorithm for approximating both the number of simple paths of length k and the number of simple cycles of size k for any k ≤ O (log n /log log log n ) in a graph with n vertices. The approximation is up to any fixed desirable relative error.

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Keywords

FOS: Computer and information sciences, Discrete Mathematics (cs.DM), Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Computer Science - Discrete Mathematics

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    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
Green
bronze