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We developed a flexible framework to generate synthetic hypergraphs, enabling better analysis of complex systems with multiple interactions. This tool aids in evaluating algorithms and understanding real-world network data.

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Area of Science:

  • Network Science
  • Data Science
  • Computational Mathematics

Background:

  • Hypergraphs are essential for modeling systems with multibody interactions beyond pairwise relationships.
  • Existing methods for generating synthetic network data are limited for hypergraphs, hindering algorithm evaluation and analysis.
  • There is a need for advanced tools to create realistic synthetic hypergraphs for research.

Purpose of the Study:

  • To propose a flexible and efficient framework for generating synthetic hypergraphs.
  • To enable the creation of hypergraphs with numerous nodes and large hyperedges.
  • To allow specification of community structures and tuning of local statistics in generated hypergraphs.

Main Methods:

  • Developed a novel framework for synthetic hypergraph generation.
  • Implemented methods to control community structures (e.g., assortative, disassortative, mixed, hard assignments).
  • Enabled tuning of various local statistics within the generated hypergraphs.

Main Results:

  • Successfully generated synthetic hypergraphs with many nodes and large hyperedges.
  • Demonstrated the framework's utility in sampling data with desired community features.
  • Showcased the application in analyzing community detection algorithms and generating structurally similar real-world hypergraphs.

Conclusions:

  • The proposed framework overcomes limitations in synthetic hypergraph generation.
  • It offers a substantial advancement for the statistical modeling of higher-order systems.
  • Facilitates standardized evaluation of algorithms and deeper study of complex networked systems.