Latent Poisson models for networks with heterogeneous density

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 which 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.

19 pages, 16 figures

References in corpus (7)

• Cooperative Game Theory Approaches for Network Partitioning
• Resolution limit in community detection
• Stochastic blockmodels and community structure in networks
• Dynamics of person-to-person interactions from distributed RFID sensor networks
• The entropy of network ensembles
• Parsimonious module inference in large networks
• The entropic origin of disassortativity in complex networks

Cited by in corpus (5)

• Hypergraph reconstruction from network data
• Merge-split Markov chain Monte Carlo for community detection
• Revealing consensus and dissensus between network partitions
• Disentangling homophily, community structure and triadic closure in networks
• Systematic assessment of the quality of fit of the stochastic block model for empirical networks