Information Theory Framework Developed for Comparing Complex Networks with Higher-Order Interactions
Scientists have published a new mathematical framework for measuring similarity between hypergraphs, which capture group interactions beyond simple pairwise connections. The work, published in Science Advances, uses normalized mutual information to compare these complex structures at multiple scales and interaction orders. The framework addresses a gap in network science, where existing comparison tools were limited to two-way relationships despite many real-world systems involving multi-way interactions.
A research team has introduced a general information-theoretic framework for quantifying similarity between hypergraphs — mathematical structures that encode interactions among three or more entities simultaneously, unlike standard networks that only represent pairwise links. The method operationalizes structural overlap between hypergraphs as a normalized mutual information measure, enabling a hierarchy of similarity formulations that vary in granularity and scale. By correcting for spurious correlations, the approach aims to produce principled comparisons rather than coincidental pattern matches. The authors validated the framework through extensive experiments on synthetic hypergraphs and demonstrated its utility on empirical higher-order networks drawn from real-world systems. The work is positioned as foundational tooling for tasks such as clustering and anomaly detection in complex systems where group dynamics — not just dyadic interactions — drive behavior. The paper appeared on arXiv in October 2025 and was subsequently published in Science Advances in 2026.
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- arXiv physicsCenter
Information theory for hypergraph similarity
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