### Abstract

Recent work on social networks has tackled the measurement and optimization of these networks' robustness and resilience to both failures and attacks. Different metrics have been used to quantitatively measure the robustness of a social network. In this work, we design and apply a Genetic Algorithm that maximizes the cyclic entropy of a social network model, hence optimizing its robustness to failures. Our social network model is a scale-free network created using Barabási and Albert's generative model, since it has been demonstrated recently that many large complex networks display a scale-free structure. We compare the cycles distribution of the optimally robust network generated by our algorithm to that belonging to a fully connected network. Moreover, we optimize the robustness of a scale-free network based on the links-degree entropy, and compare the outcomes to that which is based on cycles- entropy. We show that both cyclic and degree entropy optimization are equivalent and provide the same final optimal distribution. Hence, cyclic entropy optimization is justified in the search for the optimal network distribution.

Original language | English |
---|---|

Pages (from-to) | 983-1003 |

Number of pages | 21 |

Journal | Journal of Universal Computer Science |

Volume | 16 |

Issue number | 6 |

Publication status | Published - 18 Jun 2010 |

Externally published | Yes |

### Fingerprint

### Keywords

- Entropy
- Evolutionary algorithm
- Genetic algorithm
- Social networks

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Journal of Universal Computer Science*,

*16*(6), 983-1003.

**Entropy optimization of social networks using an evolutionary algorithm.** / Safar, Maytham; El-Sayed, Nosayba; Mahdi, Khaled; Taniar, David.

Research output: Contribution to journal › Article

*Journal of Universal Computer Science*, vol. 16, no. 6, pp. 983-1003.

}

TY - JOUR

T1 - Entropy optimization of social networks using an evolutionary algorithm

AU - Safar, Maytham

AU - El-Sayed, Nosayba

AU - Mahdi, Khaled

AU - Taniar, David

PY - 2010/6/18

Y1 - 2010/6/18

N2 - Recent work on social networks has tackled the measurement and optimization of these networks' robustness and resilience to both failures and attacks. Different metrics have been used to quantitatively measure the robustness of a social network. In this work, we design and apply a Genetic Algorithm that maximizes the cyclic entropy of a social network model, hence optimizing its robustness to failures. Our social network model is a scale-free network created using Barabási and Albert's generative model, since it has been demonstrated recently that many large complex networks display a scale-free structure. We compare the cycles distribution of the optimally robust network generated by our algorithm to that belonging to a fully connected network. Moreover, we optimize the robustness of a scale-free network based on the links-degree entropy, and compare the outcomes to that which is based on cycles- entropy. We show that both cyclic and degree entropy optimization are equivalent and provide the same final optimal distribution. Hence, cyclic entropy optimization is justified in the search for the optimal network distribution.

AB - Recent work on social networks has tackled the measurement and optimization of these networks' robustness and resilience to both failures and attacks. Different metrics have been used to quantitatively measure the robustness of a social network. In this work, we design and apply a Genetic Algorithm that maximizes the cyclic entropy of a social network model, hence optimizing its robustness to failures. Our social network model is a scale-free network created using Barabási and Albert's generative model, since it has been demonstrated recently that many large complex networks display a scale-free structure. We compare the cycles distribution of the optimally robust network generated by our algorithm to that belonging to a fully connected network. Moreover, we optimize the robustness of a scale-free network based on the links-degree entropy, and compare the outcomes to that which is based on cycles- entropy. We show that both cyclic and degree entropy optimization are equivalent and provide the same final optimal distribution. Hence, cyclic entropy optimization is justified in the search for the optimal network distribution.

KW - Entropy

KW - Evolutionary algorithm

KW - Genetic algorithm

KW - Social networks

UR - http://www.scopus.com/inward/record.url?scp=77953529575&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953529575&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:77953529575

VL - 16

SP - 983

EP - 1003

JO - Journal of Universal Computer Science

JF - Journal of Universal Computer Science

SN - 0948-6968

IS - 6

ER -