"Automated Design of Random Dynamic Graph Models for Enterprise Compute" by Aaron Scott Pope, Daniel R. Tauritz et al.
 

Automated Design of Random Dynamic Graph Models for Enterprise Computer Network Applications

Abstract

Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. 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 real-world dynamic networks.

Meeting Name

2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)

Department(s)

Computer Science

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

© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jul 2019

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