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ORIGINAL RESEARCH article
Front. Phys.
Sec. Complex Physical Systems
Volume 12 - 2024 |
doi: 10.3389/fphy.2024.1429731
This article is part of the Research Topic Motifs of Complex Networks: Methods and Applications View all 5 articles
Modelling network motifs as higher order interactions: a statistical inference based approach
Provisionally accepted- Julius Maximilian University of Würzburg, Würzburg, Germany
The prevalent approach to motif analysis seeks to describe the local connectivity structure of networks by identifying subgraph patterns that appear significantly more often in a network then expected under a null model that conserves certain features of the original network. In this article we advocate for an alternative approach based on statistical inference of generative models where nodes are connected not only by edges but also copies of higher order subgraphs. These models naturally lead to the consideration of latent states that correspond to decompositions of networks into higher order interactions in the form of subgraphs that can have the topology of any simply connected motif. Being based on principles of parsimony the method can infer concise sets of motifs from within thousands of candidates allowing for consistent detection of larger motifs. The inferential approach yields not only a set of statistically significant higher order motifs but also an explicit decomposition of the network into these motifs, which opens new possibilities for the systematic study of the topological and dynamical implications of higher order connectivity structures in networks. After briefly reviewing core concepts and methods, we provide example applications to empirical data sets and discuss how the inferential approach addresses current problems in motif analysis and explore how concepts and methods common to motif analysis translate to the inferential framework.
Keywords: network motifs, Higher order networks, statistical inference, random graph models, Network analysis, Network module division
Received: 08 May 2024; Accepted: 05 Aug 2024.
Copyright: © 2024 Wegner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Anatol E. Wegner, Julius Maximilian University of Würzburg, Würzburg, Germany
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