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Social Network Analysis using Cultural Algorithms and its Variants

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  • Fri, 02/03/2017 - 1:00pm - 4:00pm




Social Network Analysis using Cultural Algorithms and its Variants

PhD. Dissertation Defense by:

Pooya Moradian Zadeh

Date: Friday, February 3, 2017
Time: 1:00 pm - 4:00pm
Location: 3105, Lambton Tower

Abstract:Finding relationships between social entities and discovering the underlying structures of networks are fundamental tasks for analyzing social networks. In recent years, various methods have been suggested to study these networks efficiently, however, due to the dynamic and complex nature that these networks have, a lot of open problems still exist in the field.

The aim of this research is to propose an integrated computational model to study the structure and behavior of the complex social network. The focus of this research work is on two major classic problems in the field which are called community detection and link prediction. Moreover, a novel concept of population adaptation through knowledge migration in real-life social systems has been identified to model and study through the proposed method. To the best of our knowledge, this is the first work in the field which is exploring this concept through this approach.

According to the obtained results, utilizing the proposed approach in community detection problem can reduce the search space size by 80%. It also can improve the accuracy of the search process in high dense networks by up to 30% compared with the other well-known methods. Addressing the link prediction problem through the proposed approach also can reach the comparable results with other methods and predict the next state of the system with a notable high accuracy. In addition, the obtained results from the study of population adaption through knowledge migration indicate that population with prior knowledge about an environment can adapt themselves to the new environment faster than the ones who do not have this knowledge if the level of changes between the two environments is less than 15%.

Thesis Committee:
Internal Readers: Dr. Richard Frost, Dr. Dr. Jianguo Lu     
External Reader: Dr. Abdulkadir Hussein
External Examiner: Dr. Beatrice Ombuki-Berman
Advisor: Dr. Ziad Kobti
Chair: Dr. Eleanor Maticka-Tyndale



Margaret Garabon
csgradinfo@uwindsor.ca
(519)253-3000 ext.3716