Ng TLJ, Murphy TB. Model-based clustering for random hypergraphs.
ADV DATA ANAL CLASSI 2021;
16:691-723. [PMID:
36043219 PMCID:
PMC9418112 DOI:
10.1007/s11634-021-00454-7]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 06/14/2021] [Accepted: 06/20/2021] [Indexed: 11/12/2022]
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.
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