Multiway Spherical Clustering via Degree-Corrected Tensor Block Models. (arXiv:2201.07401v1 [math.ST] CROSS LISTED)

We consider the problem of multiway clustering in the presence of unknown
degree heterogeneity. Such data problems arise commonly in applications such as
recommendation system, neuroimaging, community detection, and hypergraph
partitions in social networks. The allowance of degree heterogeneity provides
great flexibility in clustering models, but the extra complexity poses
significant challenges in both statistics and computation. Here, we develop a
degree-corrected tensor block model with estimation accuracy guarantees. We
present the phase transition of clustering performance based on the …

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