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  1. روشی کارا برای تشخیص گره های مرکزی در شبکه های اجتماعی
روشی کارا برای تشخیص گره های مرکزی در شبکه های اجتماعی
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روشی کارا برای تشخیص گره های مرکزی در شبکه های اجتماعی

جز ناظمیان، علی Joz Nazemian, Ali

An Efficient Method for Identifying Central Nodes in Social Networks

Joz Nazemian, Ali | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 49002 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Movaghar, Ali
  7. Abstract:
  8. Identifying central node is an important issue in network structural analysis. Nodes with high centrality have a significant impact on spreading of influence and ideas in social networks, activity of nodes in mobile phone networks, and also act as bottlenecks in communication networks. Therefore, identifying k-highest central nodes will be of great interest in many applications. To this end, many exact and approximation algorithms have been recently proposed. The major drawback of these algorithms is that they are not efficient with respect to the tremendous size of today’s networks. Moreover, most of these algorithms assume full knowledge of the network topological structure which is not feasible in large scale social networks.In this project to overcome these shortcomings, we propose a new distributed and privacy preserving framework, called CS-HiBet, to efficiently detect the k-highest betweenness centrality nodes in a social network by using the compressed sensing theory. Compressive sensing is a new paradigm in signal processing and information theory, which proposes to sample and compress sparse signals simultaneously and has drawn much attention in recent years. Furthermore, we extend the proposed method to detect the k-highest closeness centrality.The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world datasets. The experimental results demonstrate that our approach outperforms the existing state-of-the-art methods with notable improvements in terms of FMeasure
  9. Keywords:
  10. Social Networks ; Centrality Metrics ; Identifying Central Nodes ; Network Sampling

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