Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods
This work addresses the challenge of detecting communities in hypergraphs for applications like social tagging systems, offering a theoretical guarantee but is incremental as it builds on existing tensor methods for a specific model.
The paper tackled the problem of community detection in hypergraphs, specifically in social tagging networks, by proposing a tensor decomposition approach that guarantees consistent learning of communities under efficient sample complexity and separation requirements.
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.