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Analysis the Effect of Structure on Spreading Information in Social Networks

Babaei, Mahmoud Reza | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42399 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Safari, Mohammad Ali
  7. Abstract:
  8. The dynamic behavior of networks largely depends on their structural properties. The information or failures can spread through the links of complex networks constructed by people or agents, and their physical and informational contacts. In this research, the process of network diffusion was investigated in different model and real networks. In particular, we focused on ”cascaded failure” and ”viral marketing” which are among the major topics that have attracted much attention in this context. Firstly, we investigated the robustness of modular complex networks against random and systematic component failures. Many real-world networks have modular structure and they may undergo random errors or intentional attacks in their components. More devastating situation may happen if the network components have capacity; the errors and attacks may lead to a cascaded component removal process, and consequently, the network may lose the performance. The errors and attacks may lead to a cascaded component removal process, and consequently, the network may lose its performance. In this work, we study the size of the largest connected component of the networks as cascade errors or attacks occur. The robustness of the network is tested as a function of both the inter-modular connection probability and intra-modular rewiring probability. We find that the robustness of the network is more influenced by the intra-modular connection probability in a way that the more the value of this probability the better the robustness of the network. Secondly, we investigated the problem of revenue maximization in social networks. Adopting new behavior in social networks is a strong motivation for monetizing social networks. In these networks, buyers are influenced by other buyers who already own an item. Thus the buyers’ willingness to pay for the item is considered as a function of the positive feedbacks they receive from their friends. Motivated by the fact that finding an optimal marketing strategy is NP-hard, an optimal influence model has been proposed, in which giving the item for free to a subset of influential buyers in a network increases the valuation of the other potential buyers for the item. In this work, we considered the more general problem by offering discounts instead of giving the item for free to an initial set of buyers. We show the effectiveness of the proposed discount-based strategies by computational experiments on artificially constructed model networks as well as a number of real-world networks
  9. Keywords:
  10. Pricing ; Viral Marketing ; Social Networks ; Tolerance ; Profit Maximization ; Complex Network

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