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View all- Tauritz DWoodward JKrawiec K(2021)Hyper-heuristics tutorialProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461418(528-557)Online publication date: 7-Jul-2021
Automated design of random dynamic graph models
Pages 1504 - 1512
Abstract
Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. Examples include modeling the evolution of relationships and communities in a social network or tracking the activity of users within an enterprise computer network. In the case of static graph representations, random graph models are often useful for analyzing and predicting the characteristics of a given network. Even though random dynamic graph models are a trending research topic, the field is still relatively unexplored. The selection of available models is limited and manually developing a model for a new application can be difficult and time-consuming. This work leverages hyper-heuristic techniques to automate the design of novel random dynamic graph models. A genetic programming approach is used to evolve custom heuristics that emulate the behavior of a variety of target models with high accuracy. Results are presented that illustrate the potential for the automated design of custom random dynamic graph models.
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- Automated design of random dynamic graph models
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Published In
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
- Editor:
- Manuel López-Ibáñez,
- General Chairs:
- Anne Auger,
- Thomas Stützle
Copyright © 2019 ACM.
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Published: 13 July 2019
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View all- Tauritz DWoodward JKrawiec K(2021)Hyper-heuristics tutorialProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461418(528-557)Online publication date: 7-Jul-2021
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