mlsanityData to 14 Oct 2026

Community Detection in General Hypergraph via Graph Embedding

arXiv 2103.15035 ↗First posted 28 Mar 2021stat.ML cs.LGcited by 12

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Abstract (arXiv)

Conventional network data has largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks. In this article, we propose a novel method for detecting community structure in general hypergraph networks, uniform or non-uniform. The proposed method introduces a null vertex to augment a non-uniform hypergraph into a uniform multi-hypergraph, and then embeds the multi-hypergraph in a low-dimensional vector space such that vertices within the same community are close to each other. The resultant optimization task can be efficiently tackled by an alternative updating scheme. The asymptotic consistencies of the proposed method are established in terms of both community detection and hypergraph estimation, which are also supported by numerical experiments on some synthetic and real-life hypergraph networks.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 17 of its 59 references that are in the map. First posted on arXiv 28 Mar 2021.

Builds on

17 of 59 references.

References · 17

  1. 2013Consistency of spectral clustering in stochastic block modelscited by 129
  2. 2017The landscape of the spiked tensor modelcited by 34
  3. 2020Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decompositioncited by 30
  4. 2019Community Detection for Hypergraph Networks via Regularized Tensor Power Iterationcited by 27
  5. 2016Network cross-validation by edge samplingcited by 26
  6. 2015Consistency of Spectral Hypergraph Partitioning under Planted Partition Modelcited by 23
  7. 2014Coauthorship and Citation Networks for Statisticianscited by 22
  8. 2014Network Cross-Validation for Determining the Number of Communities in Network Datacited by 21
  9. 2018Hypergraph Spectral Clustering in the Weighted Stochastic Block Modelcited by 19
  10. 2017Community Detection in Hypergraphs, Spiked Tensor Models, and Sum-of-Squarescited by 17
  11. 2016Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniquescited by 13
  12. 2018On the Minimax Misclassification Ratio of Hypergraph Community Detectioncited by 11
5 more
  1. 2018Convex Relaxation Methods for Community Detectioncited by 11
  2. 2016Probabilistic community detection with unknown number of communitiescited by 5
  3. 2017Individualized Multi-directional Variable Selectioncited by 5
  4. 2019Optimal Bayesian Estimation for Random Dot Product Graphscited by 4
  5. 2018Hypergraph Clustering: A Modularity Maximization Approachcited by 2

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Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 5; 2024: 1.

12 papers.

  1. 2021Latent Space Model for Higher-order Networks and Generalized Tensor Decompositioncited by 13
  2. 2021Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Modelcited by 7
  3. 2022Sparse random hypergraphs: Non-backtracking spectra and community detectioncited by 5
  4. 2022Community Detection in the Hypergraph SBM: Exact Recovery Given the Similarity Matrixcited by 2
  5. 2023Multilayer hypergraph clustering using the aggregate similarity matrixcited by 2
  6. 2023Optimal and exact recovery on the general nonuniform Hypergraph Stochastic Block Modelcited by 2
  7. 2021Community detection in censored hypergraphcited by 1
  8. 2023Tensorized Hypergraph Neural Networkscited by 1
  9. 2023Scalable tensor methods for nonuniform hypergraphscited by 1
  10. 2023Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Modelcited by 1
  11. 2022Efficient Estimation for Longitudinal Networks via Adaptive Mergingcited by 0
  12. 2024Network-based Neighborhood regressioncited by 0