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• W2038224551 abstract "We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obtain general bounds on the detectability of any prescribed block structure, given the number of nodes and edges in the sampled network. We also obtain that the maximum number of detectable blocks scales as $sqrt{N}$, where $N$ is the number of nodes in the network, for a fixed average degree $⟨k⟩$. We also show that the simplicity of the minimum description length approach yields an efficient multilevel Monte Carlo inference algorithm with a complexity of $O(ensuremath{tau}Nmathrm{log}N)$, if the number of blocks is unknown, and $O(ensuremath{tau}N)$ if it is known, where $ensuremath{tau}$ is the mixing time of the Markov chain. We illustrate the application of the method on a large network of actors and films with over ${10}^{6}$ edges, and a dissortative, bipartite block structure." @default.
• W2038224551 created "2016-06-24" @default.
• W2038224551 creator A5022414084 @default.
• W2038224551 date "2013-04-05" @default.
• W2038224551 modified "2023-10-14" @default.
• W2038224551 title "Parsimonious Module Inference in Large Networks" @default.
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• W2038224551 doi "https://doi.org/10.1103/physrevlett.110.148701" @default.
• W2038224551 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25167049" @default.
• W2038224551 hasPublicationYear "2013" @default.
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