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.

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

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