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• W2160832298 abstract "Author(s): Anandkumar, Anima; Sedghi, Hanie | Abstract: Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach for guaranteed learning of communities in a special class of hypergraphs modeling social tagging systems or folksonomies. A folksonomy is a tripartite 3-uniform hypergraph consisting of (user, tag, resource) hyperedges. We posit a probabilistic mixed membership community model, and prove that the tensor method consistently learns the communities under efficient sample complexity and separation requirements." @default.
• W2160832298 created "2016-06-24" @default.
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• W2160832298 date "2015-03-16" @default.
• W2160832298 modified "2023-09-27" @default.
• W2160832298 title "Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods" @default.
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