Hypergraphs are generalizations of simple graphs that allow for the representation of complex group interactions beyond pairwise relationships. Clustering coefficients, which quantify the local link density in networks, have been widely studied even for hypergraphs. However, existing definitions of clustering coefficients for hypergraphs do not fully capture the pairwise relationships within hyperedges. In this study, we propose a novel clustering coefficient for hypergraphs that addresses this limitation by transforming the hypergraph into a weighted graph and calculating the clustering coefficient on the resulting graph. Our definition reflects the local link density more accurately than existing definitions. We demonstrate through theoretical evaluation on higher-order motifs that the proposed definition is consistent with the clustering coefficient for simple graphs and effectively captures relationships within hyperedges missed by existing definitions. Empirical evaluation on real-world hypergraph datasets shows that our definition exhibits similar overall clustering tendencies as existing definitions while providing more precise measurements, especially for hypergraphs with larger hyperedges. The proposed clustering coefficient has the potential to reveal structural characteristics in complex hypergraphs that are not detected by existing definitions, leading to a deeper understanding of the underlying interaction patterns in complex hypergraphs.

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