### Abstract

We design and apply a Genetic Algorithm that maximizes the cyclic-entropy of a social network model, hence optimizing its robustness to failures. Our algorithm was applied on three types of social networks: scale-free, small-world and random networks. The three types of networks were generated using Barabasi and Albert's generative model, Watts and Strogatz's model and Erdos-Renyi's model, respectively. The maximum optimal entropy achieved among all three types was the one displayed by the small-world network, which was equal to 2.6887, corresponding to an optimal network distribution found when the initial distribution was subject to 11 random edge removals and 19 additions of random edges regardless of the initial distribution. The random-network model came next with optimal entropy equal to 2.5692, followed by the scale-free network which had optimal entropy of 2.5190. We observed by keeping track of the topology of the network and the cycles' length distribution within it, that all different types of networks evolve almost to the same network, possibly a random network, after being subject to the cyclic-entropy optimization algorithm.

Original language | English |
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Title of host publication | Proceedings of the 2009 International Conference on Computational Science and Its Applications, ICCSA 2009 |

Pages | 9-16 |

Number of pages | 8 |

DOIs | |

Publication status | Published - 23 Nov 2009 |

Externally published | Yes |

Event | 2009 International Conference on Computational Science and Its Applications, ICCSA 2009 - Yongin, Korea, Republic of Duration: 29 Jun 2009 → 2 Jul 2009 |

### Other

Other | 2009 International Conference on Computational Science and Its Applications, ICCSA 2009 |
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Country | Korea, Republic of |

City | Yongin |

Period | 29/6/09 → 2/7/09 |

### Fingerprint

### Keywords

- Cycles
- Entropy
- Social network

### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Computer Science Applications
- Software

### Cite this

*Proceedings of the 2009 International Conference on Computational Science and Its Applications, ICCSA 2009*(pp. 9-16). [5260976] https://doi.org/10.1109/ICCSA.2009.11

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 2009 International Conference on Computational Science and Its Applications, ICCSA 2009.*, 5260976, pp. 9-16, 2009 International Conference on Computational Science and Its Applications, ICCSA 2009, Yongin, Korea, Republic of, 29/6/09. https://doi.org/10.1109/ICCSA.2009.11

}

TY - GEN

T1 - Cyclic entropy optimization of social networks using an evolutionary algorithm

AU - El-Sayed, Nosayba

AU - Mahdi, Khaled

AU - Safar, Maytham

PY - 2009/11/23

Y1 - 2009/11/23

N2 - We design and apply a Genetic Algorithm that maximizes the cyclic-entropy of a social network model, hence optimizing its robustness to failures. Our algorithm was applied on three types of social networks: scale-free, small-world and random networks. The three types of networks were generated using Barabasi and Albert's generative model, Watts and Strogatz's model and Erdos-Renyi's model, respectively. The maximum optimal entropy achieved among all three types was the one displayed by the small-world network, which was equal to 2.6887, corresponding to an optimal network distribution found when the initial distribution was subject to 11 random edge removals and 19 additions of random edges regardless of the initial distribution. The random-network model came next with optimal entropy equal to 2.5692, followed by the scale-free network which had optimal entropy of 2.5190. We observed by keeping track of the topology of the network and the cycles' length distribution within it, that all different types of networks evolve almost to the same network, possibly a random network, after being subject to the cyclic-entropy optimization algorithm.

AB - We design and apply a Genetic Algorithm that maximizes the cyclic-entropy of a social network model, hence optimizing its robustness to failures. Our algorithm was applied on three types of social networks: scale-free, small-world and random networks. The three types of networks were generated using Barabasi and Albert's generative model, Watts and Strogatz's model and Erdos-Renyi's model, respectively. The maximum optimal entropy achieved among all three types was the one displayed by the small-world network, which was equal to 2.6887, corresponding to an optimal network distribution found when the initial distribution was subject to 11 random edge removals and 19 additions of random edges regardless of the initial distribution. The random-network model came next with optimal entropy equal to 2.5692, followed by the scale-free network which had optimal entropy of 2.5190. We observed by keeping track of the topology of the network and the cycles' length distribution within it, that all different types of networks evolve almost to the same network, possibly a random network, after being subject to the cyclic-entropy optimization algorithm.

KW - Cycles

KW - Entropy

KW - Social network

UR - http://www.scopus.com/inward/record.url?scp=70449630112&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449630112&partnerID=8YFLogxK

U2 - 10.1109/ICCSA.2009.11

DO - 10.1109/ICCSA.2009.11

M3 - Conference contribution

AN - SCOPUS:70449630112

SN - 9780769537016

SP - 9

EP - 16

BT - Proceedings of the 2009 International Conference on Computational Science and Its Applications, ICCSA 2009

ER -