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Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems.
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A correlation clustering framework for community detection
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Hypergraph partitioning for multiple communication cost metrics: Model and methods
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Consistency of spectral partitioning of uniform hypergraphs under planted partition model
D. Ghoshdastidar · 2014
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Efficiently inferring community structure in bipartite networks
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Hierarchical block structures and high-resolution model selection in large networks
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Scalable detection of statistically significant communities and hierarchies, using message passing for modularity
P. Zhang · 2014
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Spectral detection on sparse hypergraphs
M. C. Angelini · 2015
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Stochastic block models are a discrete surface tension
Z. M. Boyd · 2019
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Clustering via hypergraph modularity
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Community detection for hypergraph networks via regularized tensor power iteration
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From networks to optimal higher-order models of complex systems
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Bayesian stochastic blockmodeling
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HyperGCN: A new method for training graph convolutional networks on hypergraphs
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Clustering in graphs and hypergraphs with categorical edge labels
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Networks beyond pairwise interactions: structure and dynamics
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Configuration models of random hypergraphs
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Social contagion models on hypergraphs
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Community detection algorithm using hypergraph modularity
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Hypergraph clustering by iteratively reweighted modularity maximization
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Merge-split Markov chain Monte Carlo for community detection
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Modelling non-linear consensus dynamics on hypergraphs
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Community detection in bipartite networks with stochastic blockmodels
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Statistical inference of assortative community structures
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Congressional committee assignments, 103rd to 115th congresses, 1993–2017
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