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This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in...
In this paper, motivated by network inference, we introduce a general framework, called LSR-Weighted, to efficiently recover sparse characteristic of links in weighted networks. The links in many real-world networks are not only binary entities, either present or not, but rather have associated weights that record their strengths relative to one another. Such models are generally described in terms...
One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper,...
Partially-observed data collected by sampling methods is often being studied to obtain the characteristics of information diffusion networks. However, these methods usually do not consider the behavior of diffusion process. In this paper, we propose a novel two-step (sampling/estimation) measurement framework by utilizing the diffusion process characteristics. To this end, we propose a link-tracing...
The spread of information cascades over social networks forms the diffusion networks. The latent structure of diffusion networks makes the problem of extracting diffusion links difficult. As observing the sources of information is not usually possible, the only available prior knowledge is the infection times of individuals. We confront these challenges by proposing a new method called \textsc{Dne}...
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