Abstract
Real-world complex systems often involve interactions among more than two nodes, and such complex systems can be represented by hypergraphs. Comparison between a given hypergraph and randomized hypergraphs that preserve specific properties reveal effects or dependencies of the properties on the structure and dynamics. In this study, we extend an existing family of reference models for hypergraphs to generate randomized hypergraphs that preserve the pairwise joint degree distribution and the degree-dependent two-mode clustering coefficient of the original hypergraph. Using empirical hypergraph data sets, we numerically show that the extended model preserves the properties of the node and hyperedge as designed.
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Miyashita, R., Nakajima, K., Fukuda, M., Shudo, K. (2023). Random Hypergraph Model Preserving Two-Mode Clustering Coefficient. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_18
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DOI: https://doi.org/10.1007/978-3-031-39831-5_18
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