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Information and Influence Diffusion in Social Network

Sepehr, Arman | 2022

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 55313 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Beigy, Hamid
  7. Abstract:
  8. People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a u/rof cascade? If so, can we find a set of users that are more likely to trigger viral cascades? There are many factors influenced the message virality. In this thesis, we investigate the effect of graph structure, diffusion pattern as well as the message text on virality measure. Finally, the authors solve both source localization and inferring COVID-19 network via propsed methods.First, the authors investigate probability estimation and maximization of cascade virality. In this section, we develop an efficient viral cascade probability estimation method, VICE, that leverages an special i,m,portan,ce sam,pli,ng approximation to achieve high accuracy, even in the cases of very .stn,aff prohoh(I(/y of influence. Second, the authors verify the effect of diffusion pattern on the virality. This section aims to answer the following questions: how does a spread like a viral cascade propagate in a network? Do the structural properties of the propagation pattern play a role in virality? If so, can we shape the propagation pattern to maximize the final influence? Finally, we solve source localization and inferring COVID-19 network problem.The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. Then, the authors propose a precise approach to detect the most infected country, which is responsible for spreading the virus to other countries. As a first step, we examine the temporal dynamics of disease spread across different countries in this paper. Then, we present a novel approach to infer dynamic epidemiological disease networks. The main question in this study is which countries are responsible to spread each variant of coronavirus to other countries?
  9. Keywords:
  10. Viral Marketing ; Diffusion Network ; Social Networks ; Information Diffusion ; Stochastic Model ; Influence Maximization ; Social Influence Spread

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