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Author ORCID Identifier
https://orcid.org/0000-0002-7542-9800
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
Year Degree Awarded
2020
Month Degree Awarded
September
First Advisor
Andrew McGregor
Second Advisor
Phillipa Gill
Third Advisor
Markos Katsoulakis
Fourth Advisor
Cameron Musco
Subject Categories
OS and Networks | Theory and Algorithms
Abstract
A long-standing assumption common in algorithm design is that any part of the input is accessible at any time for unit cost. However, as we work with increasingly large data sets, or as we build smaller devices, we must revisit this assumption. In this thesis, I present some of my work on graph algorithms designed for circumstances where traditional assumptions about inputs do not apply.
1. Classical graph algorithms require direct access to the input graph and this is not feasible when the graph is too large to fit in memory. For computation on massive graphs we consider the dynamic streaming graph model. Given an input graph defined by as a stream of edge insertions and deletions, our goal is to approximate properties of this graph using space that is sublinear in the size of the stream. In this thesis, I present algorithms for approximating vertex connectivity, hypergraph edge connectivity, maximum coverage, unique coverage, and temporal connectivity in graph streams.
2. In certain applications the input graph is not explicitly represented, but its edges may be discovered via queries which require costly computation or measurement. I present two open-source systems which solve real-world problems via graph algorithms which may access their inputs only through costly edge queries. M ESH is a memory manager which compacts memory efficiently by finding an approximate graph matching subject to stringent time and edge query restrictions. PathCache is an efficiently scalable network measurement platform that outperforms the current state of the art.
DOI
https://doi.org/10.7275/g94z-xg57
Recommended Citation
Tench, David, "ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS" (2020). Doctoral Dissertations. 2084.
https://doi.org/10.7275/g94z-xg57
https://scholarworks.umass.edu/dissertations_2/2084
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.