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A Novel Discrete-Time Information Propagation Model over Networks with Deviation of Infection Rates
A Novel Discrete-Time Information Propagation Model over Networks with Deviation of Infection Rates
Hoàng Phi Dũng
Computer Networks and Online Social Networks have become fundamental infrastructures for global information dissemination, encompassing systems and platforms such as the Internet, Facebook, TikTok, and Twitter... Owing to their large scale and high connectivity, these networks provide favorable environments for the propagation of computer viruses and the diffusion of harmful information. One of the widely adopted approaches for reducing the extent of infection in a network is to remove selected edges and deactivate certain nodes. This leads to a fundamental problem in network analysis: identifying the most critical edges and the most influential nodes, either in the entire network or within a specific community from a set of seeds.
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