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• W3006409324 abstract "Empirical networks are often globally sparse, with a small average number of connections per node, when compared to the total size of the network. However, this sparsity tends not to be homogeneous, and networks can also be locally dense, for example, with a few nodes connecting to a large fraction of the rest of the network, or with small groups of nodes with a large probability of connections between them. Here we show how latent Poisson models that generate hidden multigraphs can be effective at capturing this density heterogeneity, while being more tractable mathematically than some of the alternatives that model simple graphs directly. We show how these latent multigraphs can be reconstructed from data on simple graphs, and how this allows us to disentangle disassortative degree-degree correlations from the constraints of imposed degree sequences, and to improve the identification of community structure in empirically relevant scenarios." @default.
• W3006409324 created "2020-02-24" @default.
• W3006409324 creator A5022414084 @default.
• W3006409324 date "2020-07-17" @default.
• W3006409324 modified "2023-10-10" @default.
• W3006409324 title "Latent Poisson models for networks with heterogeneous density" @default.
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• W3006409324 doi "https://doi.org/10.1103/physreve.102.012309" @default.
• W3006409324 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32794993" @default.
• W3006409324 hasPublicationYear "2020" @default.
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