Model-based clustering for random hypergraphs

Tin Lok James Ng () and Thomas Brendan Murphy
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Tin Lok James Ng: Trinity College Dublin
Thomas Brendan Murphy: University College Dublin

Advances in Data Analysis and Classification, 2022, vol. 16, issue 3, No 8, 723 pages

Abstract: Abstract A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension of the latent class analysis model that introduces two clustering structures for hyperedges and captures variation in the size of hyperedges. An expectation maximization algorithm with minorization maximization steps is developed to perform parameter estimation. Model selection using Bayesian Information Criterion is proposed. The model is applied to simulated data and two real-world data sets where interesting results are obtained.

Keywords: Clustering; Hypergraph; Latent class analysis; Minorization maximization; 62H99; 62P25 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11634-021-00454-7

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