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• W3090503316 abstract "Access to complete data in large scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks do not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. The present paper tries to infer the unobserved data from both diffusion network and network structure by learning a model from the partially observed data. We develop a probabilistic generative model called DiffStru to jointly discover the hidden links of network structure and the omitted diffusion activities. The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled low dimensional latent factors. In addition to inferring the unseen data, the learned latent factors may also help network classification problems such as community detection. Simulation results on synthetic and real-world datasets show the excellent performance of the proposed method in terms of link prediction and discovering the identity and infection time of invisible social behaviors." @default.
• W3090503316 created "2020-10-08" @default.
• W3090503316 creator A5039988490 @default.
• W3090503316 creator A5061656766 @default.
• W3090503316 creator A5063512925 @default.
• W3090503316 date "2020-10-03" @default.
• W3090503316 modified "2023-09-27" @default.
• W3090503316 title "Joint Inference of Structure and Diffusion in Partially Observed Social Networks." @default.
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• W3090503316 hasPublicationYear "2020" @default.
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