TL;DR
This paper introduces a versatile framework for generating synthetic hypergraphs with community structures, enabling better evaluation and analysis of algorithms in systems with complex multi-body interactions.
Contribution
It presents a novel, flexible method for creating large hypergraphs with customizable community features, addressing a gap in synthetic hypergraph data generation.
Findings
Allows sampling hypergraphs with specific community structures
Enables analysis of community detection algorithms on synthetic data
Generates hypergraphs similar to real-world higher-order systems
Abstract
In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), analyze community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming…
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