Michael T. GoodrichjS Edward F. Grovefll Roberto Tamassia*t Darren Erik Vengrofftt Jeffrey Scott Vitterqt We present a collection of new techniques for designing and analyzing efficient external-memory algorithms for graph problems and illustrate how these techniques can be applied to a wide variety of specific problems. Our results include: Proximate-neighboring. We present a simple method for deriving external-memory lower bounds via reductions from a problem we call the “proximate neighbors” problem. We use this technique to derive non-trivial lower bounds for such problems as list ranking, expression tree evaluation, and connected components. PRAM simulation. We give methods for efficiently simulating PRAM computations in external memory, even for some cases in which the PRAM algorithm is not work-optimal. We apply this to derive a number of optimal (and simple) external-memory graph algorithms. Time-forward processing. We present a general technique for evaluating circuits (or “circuit-like” computations) in external memory. We also use *Department of Computer Science, Box 1910, Brown University, Providence, RI 02912-1910. *Supported in part by the National Science Foundation, by the U.S. Army Research Office, and by the Advanced Research

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@inproceedings{Chiang1995ExternalMemoryGA, title={External-Memory Graph Algorithms}, author={Yi-Jen Chiang and Michael T. Goodrich and Edward F. Grove and Roberto Tamassia and Darren Erik Vengroff and Jeffrey Scott Vitter}, booktitle={SODA}, year={1995} }