ArticleNature communications2025
Mining higher-order triadic interactions.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Structure Guides Function: Beyond Pairwise Neural Interactions.The Journal of comparative neurology · 2026Review
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Biological detail and graph structure in network neuroscience.Frontiers in network physiology · 2025Review
- Triadic percolation induces dynamical topological patterns in higher-order networks.PNAS nexus · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Complex systems often involve higher-order interactions that go beyond pairwise networks. Triadic interactions, where one node regulates the interaction between two others, are a fundamental form of higher-order dynamics found in many biological systems, from neuron-glia communication to gene regulation and ecosystems. However, triadic interactions have so far been mostly neglected. In this article, we propose the Triadic Perceptron Model (TPM) which shows that triadic interactions can modulate the mutual information between the dynamical states of two connected nodes. Leveraging this result, we formulate the Triadic Interaction Mining (TRIM) algorithm to extract triadic interactions from node metadata, and we apply this framework to gene expression data, finding new candidates for triadic interactions relevant for Acute Myeloid Leukemia. Our findings highlight crucial aspects of triadic interactions that are often ignored, offering a framework that can deepen our understanding of complex systems across biology, ecology, and climate science.
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