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Updated: Jun 5, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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
PubMed
Summary
This summary is machine-generated.

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.

Related Experiment Videos

Last Updated: Jun 5, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

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.