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Computer Science > Social and Information Networks
Title: Nonparametric inference of higher order interaction patterns in networks
(Submitted on 22 Mar 2024 (v1), last revised 2 Apr 2024 (this version, v2))
Abstract: We propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs.The method is based on a class of analytically solvable generative models, where vertices are connected via explicit copies of motifs, which in combination with non-parametric priors allow us to infer higher order interactions from dyadic graph data without any prior knowledge on the types or frequencies of such interactions. Crucially, we also consider 'degree--corrected' models that correctly reflect the degree distribution of the network and consequently prove to be a better fit for many real world--networks compared to non-degree corrected models. We test the presented approach on simulated data for which we recover the set of underlying higher order interactions to a high degree of accuracy. For empirical networks the method identifies concise sets of atomic subgraphs from within thousands of candidates that cover a large fraction of edges and include higher order interactions of known structural and functional significance. The method not only produces an explicit higher order representation of the network but also a fit of the network to analytically tractable models opening new avenues for the systematic study of higher order network structures.
Submission history
From: Anatol Eugen Wegner [view email][v1] Fri, 22 Mar 2024 22:22:03 GMT (1940kb,D)
[v2] Tue, 2 Apr 2024 08:42:01 GMT (4395kb,D)
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