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Adaptive Matching for Expert Systems with Uncertain Task Types
To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches.
Non-Backtracking Spectrum of Degree-Corrected Stochastic Block Models
As a result, a clustering positively-correlated with the true communities can be obtained based on the second eigenvector of $B$ in the regime where $\mu_2^2 > \rho.$ In a previous work we obtained that detection is impossible when $\mu_2^2 < \rho,$ meaning that there occurs a phase-transition in the sparse regime of the Degree-Corrected Stochastic Block Model.
An Impossibility Result for Reconstruction in a Degree-Corrected Planted-Partition Model
We consider the Degree-Corrected Stochastic Block Model (DC-SBM): a random graph on $n$ nodes, having i. i. d.
A spectral method for community detection in moderately-sparse degree-corrected stochastic block models
In particular, it does not need to know the number of communities.
