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Saturday, March 30, 2019

Driving Information Diffusion

Never thought it was just about central users, but it helps to create uniformly consistent reactions.   

Even Central Users Do Not Always Drive Information Diffusion
By Chao Gao, Zhen Su, Jiming Liu, Jürgen Kurths
Communications of the ACM, February 2019, Vol. 62 No. 2, Pages 61-67

Community structure, a significant and useful statistical characteristic, is ubiquitous in social networks.17 Based on it, a network can be viewed as consisting of multiple units. The nodes (users) are highly connected to each other inside a unit, while the connections between units are sparse.4,17 For example, people with similar interests or backgrounds might join together to form a community or web-pages with related topics might cluster together. Different types of information, including rumors,5 virus attacks,10 and even cyber epidemics diffuse through social networks,8 possibly leading to unexpected social effects. A typical example is the worldwide cyberattack by WannaCry ransomware, as first reported May 12, 2017, that resulted in the infections of more than 200,000 organizations worldwide.15 The underlying attack reflects a malicious diffusion in the presence of communities; that is, the homogeneous feature of individuals leads to the community's vulnerability. It is against this back-drop that understanding the potential dynamics could help network administrators gain insight into controlling unwanted information diffusion. Much research today involves networks with community structure (such as to detect potential communities,21 model diffusion dynamics,6 and control information dissemination and sharing19). In particular, the influence of each node in the diffusion process must be taken into consideration. In simulation experiments, the source nodes that trigger diffusion are selected by researchers at random from a network or based on predefined measures of centrality. ... " 

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