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Sampling of Complex-Networks by Considering Activity-Level of Node

Khodadadi, Ali | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44080 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Manzuri Shalmani, Mohammad Taghi
  7. Abstract:
  8. Many studies has been focused on extracting structural and behavioral properties of complex networks in recent decade. Online Social Networks (OSNs) are one example of complex networks. Nowadays with rapid growth of OSNs such as Facebook and Twitter, the study of OSNs has become an interesting research area. Many of recent OSN studies studied friendship networks. Friendship network is a binary unweighted network, and all of its links have the same importance. But, in reality not only all friendship links are not representative of social interactions, but also the social links have a variety of intimacy, intensity, and etc. So, all links should not be considered equal. Recently researchers introduced activity network for considering this diversity. Activity network is constructed based on interactions between people and its links are indicator of real interactions. Recently the study of activity in OSNs has become a hot research topic. But, because of reasons such as the huge size of OSNs and their massive interaction data, most of these studies done over incomplete samples of networks. There is two main problems in previous studies of activity networks. First, there is not a simple defintion of activity network in previous studies, this is due to many activity mechanisms of these networks. Second, previous studies of activity network did not considered the differences between sample and main network. We studied sampling from activity networks, in this thesis and tried to solve the above problems. So, considering many definitions of activity in previous studies and OSNs, we proposed a framework for defining activity network as a weighted network. The proposed framework contains all definitions af activity in previous studies. Then, we proposed methods for sampling from activity networks. The proposed methods can estimate structural and behaviral properties of activity network accurately
  9. Keywords:
  10. Complex Network ; Social Networks ; Sampling ; Activity Network

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