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.
Recommended Citation
A. S. Pope et al., "Automated Design of Random Dynamic Graph Models," GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, pp. 1504 - 1512, Association for Computing Machinery, Jul 2019.
The definitive version is available at https://doi.org/10.1145/3319619.3326859
Department(s)
Computer Science
Publication Status
Public Access
Keywords and Phrases
Dynamic graphs; Genetic programming; Random graph models
International Standard Book Number (ISBN)
978-145036748-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
13 Jul 2019