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Efficient computation of implicit representations of sparse graphs (revised version)

MPG-Autoren
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Arikati,  Srinivasa R.
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Maheshwari,  Anil
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Zaroliagis,  Christos
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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MPI-I-95-1-013.pdf
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Zitation

Arikati, S. R., Maheshwari, A., & Zaroliagis, C.(1995). Efficient computation of implicit representations of sparse graphs (revised version) (MPI-I-1995-1-013). Saarbrücken: Max-Planck-Institut für Informatik.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0014-A704-1
Zusammenfassung
The problem of finding an implicit representation for a graph such that vertex adjacency can be tested quickly is fundamental to all graph algorithms. In particular, it is possible to represent sparse graphs on $n$ vertices using $O(n)$ space such that vertex adjacency is tested in $O(1)$ time. We show here how to construct such a representation efficiently by providing simple and optimal algorithms, both in a sequential and a parallel setting. Our sequential algorithm runs in $O(n)$ time. The parallel algorithm runs in $O(\log n)$ time using $O(n/{\log n})$ CRCW PRAM processors, or in $O(\log n\log^*n)$ time using $O(n/\log n\log^*n)$ EREW PRAM processors. Previous results for this problem are based on matroid partitioning and thus have a high complexity.