Related Experiment Video
Updated: Jun 5, 2026
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Information theory for hypergraph similarity
Helcio Felippe1, Alec Kirkley2,3,4, Federico Battiston1,5
1Department of Network and Data Science, Central European University, Vienna, Austria.
Science Advances
|June 3, 2026
Summary
We introduce a novel framework for comparing complex networks, moving beyond simple pairs to analyze higher-order interactions. This method provides a principled way to measure hypergraph similarity, revealing deeper structural patterns in networked systems.
Area of Science:
- Network science
- Information theory
- Complex systems analysis
Background:
- Network comparison is crucial for tasks like clustering and anomaly detection.
- Traditional methods focus on pairwise interactions, neglecting critical higher-order relationships in complex systems.
Purpose of the Study:
- To develop a general information-theoretic framework for hypergraph similarity.
- To enable principled comparison of higher-order interactions in complex networks.
Main Methods:
- Constructed a framework for hypergraph similarity based on normalized mutual information.
- Operationalized structural overlap among hypergraphs to capture higher-order interactions.
- Derived a hierarchy of similarity measures at multiple scales and interaction orders.
Main Results:
- The framework effectively captures meaningful correspondences in higher-order interactions.
- Validated through experiments on synthetic and empirical higher-order networks.
- Revealed significant patterns in complex systems with non-dyadic interactions.
Conclusions:
- Provides foundational tools for principled comparison of higher-order networks.
- Enhances understanding of structural organization in systems with complex interactions.
- Enables more accurate analysis of networked systems beyond pairwise relationships.
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