Atomic subgraphs and the statistical mechanics of networks

Abstract

We develop random graph models where graphs are generated by connecting not only pairs of vertices by edges but also larger subsets of vertices by copies of small atomic subgraphs of arbitrary topology. This allows the for the generation of graphs with extensive numbers of triangles and other network motifs commonly observed in many real world networks. More specifically we focus on maximum entropy ensembles under constraints placed on the counts and distributions of atomic subgraphs and derive general expressions for the entropy of such models. We also present a procedure for combining distributions of multiple atomic subgraphs that enables the construction of models with fewer parameters. Expanding the model to include atoms with edge and vertex labels we obtain a general class of models that can be parametrized in terms of basic building blocks and their distributions that includes many widely used models as special cases. These models include random graphs with arbitrary distributions of subgraphs, random hypergraphs, bipartite models, stochastic block models, models of multilayer networks and their degree corrected and directed versions. We show that the entropy for all these models can be derived from a single expression that is characterized by the symmetry groups of atomic subgraphs.

15 pages, 2 figures

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Cited by in corpus (5)

• Disentangling homophily, community structure and triadic closure in networks
• Entropy of labeled versus unlabeled networks
• Entropy-based models to randomize real-world hypergraphs
• Statistical physics of exchangeable sparse simple networks, multiplex networks and simplicial complexes
• Compression-based inference of network motif sets