Synthical logo
Your space
From arXiv

Nonparametric inference of higher order interaction patterns in networks

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.
Updated on April 2, 2024
Copy BibTeX
Version history
Loading PDF…
There is no AI-powered summary yet, because we do not have a budget to generate summaries for all articles.
1. Buy subscription
We will thank you for helping thousands of people to save their time at the top of the generated summary.
If you buy our subscription, you will be able to summarize multiple articles.
Pay $undefined
≈10 summaries
Pay $undefined
≈60 summaries
2. Share on socials
If this article gets to top-5 in trends, we'll summarize it for free.
Copy link