A spectral clustering approach, termed Spectral Clustering for Mixed Membership, based on an ideal cone structure of the variant for the eigendecomposition of the regularized Laplacian matrix is proposed, which shows the asymptotic consistency of the algorithm under mild assumptions by providing error bounds for the inferred membership vectors of each node.
This study explores an alternative condition-free analysis for the clustering quality of DCSC from a pure spectral view, without any random graph models, and proposes ASCENT (Adaptive Spectral Clustering with Node-wise correcTion), a simple yet effective extension of DCSC.
This work proves that a simple spectral clustering algorithm based on the diagonal-deleted Gram matrix achieves exact recovery with high probability under mild conditions on sparsity, community balance, and the number of clusters.
It is established that spectral clustering of the sample covariance matrix achieves exact recovery of the underlying communities under a stochastic blockmodel even when the adjacency matrix is unobserved.
Brendan Martin, Joshua Agterberg, Mihai Cucuringu et al.· 0 citations
By connecting core linear algebra with practical structure discovery, the review clarifies both the continuing value and the limits of the spectral perspective.
Ji Li· Theoretical and Natural Scie...· 0 citations
DBSPEC is a density-based spectral clustering algorithm that requires only approximate localization of the informative eigenvalue and is robust to poor eigenvalue separation, overcoming restrictions to homogeneous toroidal models in prior works.
Konstantin Avrachenkov, L. Sibemberg, A. Van Werde· 0 citations
A fuzzy network jump model for clustering time-varying observations indexed by the nodes of a weighted graph that accurately recovers the true membership probabilities and outperforms competing clustering methods is introduced.
Federico P. Cortese· 0 citations
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