Aaron Scott Pope
Daniel R. Tauritz
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.
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Cross Ref
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182--197. Google ScholarDigital Library
- Petter Holme and Jari Saramäki. 2012. Temporal networks. Physics Reports 519, 3 (2012), 97--125.Google ScholarCross Ref
- Aaron S. Pope, Daniel R. Tauritz, and Alexander D. Kent. 2016. Evolving Random Graph Generators: A Case for Increased Algorithmic Primitive Granularity. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 1--8.Google Scholar
- Melissa J. M. Turcotte, Alexander D. Kent, and Curtis Hash. 2018. Unified Host and Network Data Set. World Scientific, Chapter Chapter 1, 1--22.Google Scholar
Index Terms
- Automated design of random dynamic graph models for enterprise computer network applications
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